Project Content

  1. Data visulization
  2. Prediction of the recovery time
  3. Risk levels of the cities
  4. Trend prediction of the infection numbers
  5. Analyze the location of the cases and patients

孔祥喆(11811005)    林子昭(11812534)

All content is completed by us working together.

proj

Mandatory Task 1: Data statistics and visualization

In this task,I mainly analyze document PatientInfo.csv,Time.csv,SearchTrend.csv,TimeAge.csv,TimeGender.csv and TimeProvince.csv.I do lots of data statistics and visualization to make data be clear for each document.As a consequence, we can find something interesting by some pictures.

Analyze the document PatientInfo.csv

In [1]:
import pandas as pd
import numpy as np
dataset = pd.read_csv(r"C:\Users\hp\Desktop\PatientInfo.csv")
In [2]:
dataset.head() #Preview the document
Out[2]:
patient_id global_num sex birth_year age country province city disease infection_case infection_order infected_by contact_number symptom_onset_date confirmed_date released_date deceased_date state
0 1000000001 2.0 male 1964.0 50s Korea Seoul Gangseo-gu NaN overseas inflow 1.0 NaN 75.0 2020/1/22 2020/1/23 2020/2/5 NaN released
1 1000000002 5.0 male 1987.0 30s Korea Seoul Jungnang-gu NaN overseas inflow 1.0 NaN 31.0 NaN 2020/1/30 2020/3/2 NaN released
2 1000000003 6.0 male 1964.0 50s Korea Seoul Jongno-gu NaN contact with patient 2.0 2.002000e+09 17.0 NaN 2020/1/30 2020/2/19 NaN released
3 1000000004 7.0 male 1991.0 20s Korea Seoul Mapo-gu NaN overseas inflow 1.0 NaN 9.0 2020/1/26 2020/1/30 2020/2/15 NaN released
4 1000000005 9.0 female 1992.0 20s Korea Seoul Seongbuk-gu NaN contact with patient 2.0 1.000000e+09 2.0 NaN 2020/1/31 2020/2/24 NaN released
In [3]:
dataset.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3128 entries, 0 to 3127
Data columns (total 18 columns):
 #   Column              Non-Null Count  Dtype
---  ------              --------------  -----
 0   patient_id          3128 non-null   int64
 1   global_num          1968 non-null   float64
 2   sex                 3034 non-null   object
 3   birth_year          2664 non-null   float64
 4   age                 3023 non-null   object
 5   country             3038 non-null   object
 6   province            3128 non-null   object
 7   city                3052 non-null   object
 8   disease             18 non-null     object
 9   infection_case      2309 non-null   object
 10  infection_order     31 non-null     float64
 11  infected_by         735 non-null    float64
 12  contact_number      589 non-null    float64
 13  symptom_onset_date  446 non-null    object
 14  confirmed_date      3128 non-null   object
 15  released_date       981 non-null    object
 16  deceased_date       56 non-null     object
 17  state               3128 non-null   object
dtypes: float64(5), int64(1), object(12)
memory usage: 440.0+ KB
In [4]:
dataset.describe()  #A preliminary description of the data
Out[4]:
patient_id global_num birth_year infection_order infected_by contact_number
count 3.128000e+03 1968.000000 2664.000000 31.000000 7.350000e+02 589.000000
mean 3.562811e+09 6234.729167 1974.456832 2.387097 2.557286e+09 18.908319
std 2.210168e+09 3297.461518 20.250896 1.453213 1.645931e+09 76.652155
min 1.000000e+09 1.000000 1916.000000 1.000000 1.000000e+09 0.000000
25% 1.300000e+09 3624.750000 1960.000000 1.000000 1.200000e+09 2.000000
50% 2.000001e+09 7437.500000 1974.000000 2.000000 2.000000e+09 4.000000
75% 6.001001e+09 9085.250000 1993.000000 3.000000 4.100000e+09 14.000000
max 7.000000e+09 10329.000000 2020.000000 6.000000 6.100000e+09 1160.000000
In [5]:
dataset.isnull()   #Find the null value
Out[5]:
patient_id global_num sex birth_year age country province city disease infection_case infection_order infected_by contact_number symptom_onset_date confirmed_date released_date deceased_date state
0 False False False False False False False False True False False True False False False False True False
1 False False False False False False False False True False False True False True False False True False
2 False False False False False False False False True False False False False True False False True False
3 False False False False False False False False True False False True False False False False True False
4 False False False False False False False False True False False False False True False False True False
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
3123 False False False True False False False False True False True True False True False True True False
3124 False False False True False False False False True False True True False True False True True False
3125 False True False False False False False False True False True True False True False True True False
3126 False True False True False False False False True False True True False True False True True False
3127 False True False True False False False False True False True True False True False True True False

3128 rows × 18 columns

In [6]:
dataset.isnull().any(axis=0)#See if each column has an missing value
Out[6]:
patient_id            False
global_num             True
sex                    True
birth_year             True
age                    True
country                True
province              False
city                   True
disease                True
infection_case         True
infection_order        True
infected_by            True
contact_number         True
symptom_onset_date     True
confirmed_date        False
released_date          True
deceased_date          True
state                 False
dtype: bool

After a preliminary analysis of the data,I choose three characteristics of dataset and use appropriate methods to fill in the missing values

In [7]:
dataset['global_num'].fillna(method='pad') #使用前一个数值替代空值或者NA，就是NA前面最近的非空数值替换
dataset['sex'].fillna(method='pad') #使用前一个数值替代空值或者NA，就是NA前面最近的非空数值替换
dataset['contact_number'].fillna(method='pad') #使用前一个数值替代空值或者NA，就是NA前面最近的非空数值替换
Out[7]:
0       75.0
1       31.0
2       17.0
3        9.0
4        2.0
        ...
3123    20.0
3124    23.0
3125    26.0
3126    25.0
3127    14.0
Name: contact_number, Length: 3128, dtype: float64
In [8]:
from matplotlib import pyplot as plt
In [9]:
dataset['sex'].value_counts() #Count patients of different genders
Out[9]:
female    1707
male      1327
Name: sex, dtype: int64
In [10]:
dataset['infection_case'].value_counts()#Count patients of different infection_case
Out[10]:
contact with patient                     862
etc                                      499
overseas inflow                          475
Guro-gu Call Center                      112
Shincheonji Church                       105
Onchun Church                             33
Bonghwa Pureun Nursing Home               31
gym facility in Cheonan                   30
Ministry of Oceans and Fisheries          28
Cheongdo Daenam Hospital                  21
Dongan Church                             17
Eunpyeong St. Mary's Hospital             16
Gyeongsan Seorin Nursing Home             14
Seongdong-gu APT                          13
Gyeongsan Jeil Silver Town                12
Milal Shelter                             11
Gyeongsan Cham Joeun Community Center     10
Geochang Church                            6
Changnyeong Coin Karaoke                   4
gym facility in Sejong                     4
Suyeong-gu Kindergarten                    3
Pilgrimage to Israel                       2
River of Grace Community Church            1
Name: infection_case, dtype: int64

Visualize the number of different genders

In [11]:
plt.figure(figsize=(3, 8), dpi=80)
x=['male','female']
y=[1327,1707]
plt.bar(x,y,width=0.5, color=['deepskyblue','mediumslateblue'])
for a,b in zip(x,y):
    plt.text(a, b+0.05, '%.0f' % b, ha='center', va= 'bottom',fontsize=15)
plt.xticks(fontsize=15)
plt.xlabel('sex',fontsize=20)
plt.ylabel('number',fontsize=20)
plt.yticks(fontsize=15)
plt.title('Different Sex Statistics',fontsize=20)
plt.show()
In [12]:
import matplotlib.pyplot as plt
import matplotlib

label_list = ["female", "male"]
size = [1707,1327]
color = ["mediumorchid", "deepskyblue"]
explode = [0.04, 0.04]

patches, l_text, p_text = plt.pie(size, explode=explode, colors=color, labels=label_list, labeldistance=1.1, autopct="%1.1f%%", shadow=True, startangle=90, pctdistance=0.6)
plt.axis("equal")
plt.title("Percentage",fontsize=25)
plt.legend()
for t in l_text:
    t.set_size(20)
for t in p_text:
    t.set_size(20)
plt.show()

By the picutres above,we can see the number of female patients is slightly higher than the number of male patients.

Visualize the number of different age

In [13]:
import seaborn as sns
In [14]:
dataset['age'].value_counts()
Out[14]:
20s     722
50s     562
40s     432
30s     389
60s     365
70s     189
80s     149
10s     126
90s      44
0s       43
66s       1
100s      1
Name: age, dtype: int64
In [15]:
plt.figure(figsize=(10, 8), dpi=80)
x=['0s','10s','20s','30s','40s','50s','60s','70s','80s','90s','100s']
y=[43,126,722,389,432,562,366,189,149,44,1]
sns.barplot(x,y, palette='BuPu_r')
plt.xticks(fontsize=15)
plt.xlabel('Age',fontsize=20)
plt.ylabel('number',fontsize=20)
plt.yticks(fontsize=15)
plt.title('Different Age Statistics',fontsize=20)
plt.show()
In [16]:
import matplotlib.pyplot as plt
import matplotlib
from  matplotlib import cm
plt.figure(figsize=(7,12))
label_list = ["0s", "10s","20s","30s","40s","50s","60s","70s","80s","90s","100s"]    # 各部分标签
size = [43,126,722,389,432,562,366,189,149,44,1]    # 各部分大小
color = ["cyan", "darkturquoise","cadetblue","powderblue","deepskyblue","skyblue","lightskyblue","steelblue","dodgerblue","royalblue","darkorchid","violet"]     # 各部分颜色
explode = [0.01, 0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01]
colorss = cm.rainbow(np.arange(len(size))/len(size))
patches, l_text, p_text = plt.pie(size, explode=explode, colors=colorss, labels=label_list, labeldistance=1.05, autopct="%1.1f%%", shadow=True, startangle=90,radius=50, pctdistance=0.6)
plt.axis("equal")    # 设置横轴和纵轴大小相等，这样饼才是圆的
plt.title("Percentage",fontsize=20)
plt.legend()
for t in l_text:
    t.set_size(10)
for t in p_text:
    t.set_size(10)
plt.show()

By the pictures above,we can see a rough distribution of different age.Among it 20s and 50s are two main parts in patients.

Visualize male and female patients of different ages

In [17]:
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from pandas import Series,DataFrame
In [18]:
sns.set()
dfd=pd.read_csv(r"C:\Users\hp\Desktop\PatientInfo.csv")

Count male

In [19]:
import csv
cout1=0
cout2=0
cout3=0
cout4=0
cout5=0
cout6=0
cout7=0
cout8=0
cout9=0
cout10=0
cout11=0
cout12=0
with open(r"C:\Users\hp\Desktop\PatientInfo.csv",'r') as csvfile:
    reader = csv.DictReader(csvfile)
    for row in reader:
        if(row["sex"]=='male'and row["age"]=='0s'):
            cout1=cout1+1
        if(row["sex"]=='male' and row["age"]=='10s'):
            cout2=cout2+1
        if(row["sex"]=='male' and row["age"]=='20s'):
            cout3=cout3+1
        if(row["sex"]=='male' and row["age"]=='30s'):
            cout4=cout4+1
        if(row["sex"]=='male' and row["age"]=='40s'):
            cout5=cout5+1
        if(row["sex"]=='male' and row["age"]=='50s'):
            cout6=cout6+1
        if(row["sex"]=='male' and row["age"]=='60s'):
            cout7=cout7+1
        if(row["sex"]=='male' and row["age"]=='66s'):
            cout8=cout8+1
        if(row["sex"]=='male' and row["age"]=='70s'):
            cout9=cout9+1
        if(row["sex"]=='male' and row["age"]=='80s'):
            cout10=cout10+1
        if(row["sex"]=='male' and row["age"]=='90s'):
            cout11=cout11+1
        if(row["sex"]=='male' and row["age"]=='100s'):
            cout12=cout12+1

print(cout1)
print(cout2)
print(cout3)
print(cout4)
print(cout5)
print(cout6)
print(cout7)
print(cout8)
print(cout9)
print(cout10)
print(cout11)
print(cout12)

27
71
342
197
168
219
164
1
72
47
14
0

count female

In [20]:
import csv
cout1=0
cout2=0
cout3=0
cout4=0
cout5=0
cout6=0
cout7=0
cout8=0
cout9=0
cout10=0
cout11=0
cout12=0
with open(r"C:\Users\hp\Desktop\PatientInfo.csv",'r') as csvfile:
    reader = csv.DictReader(csvfile)
    for row in reader:
        if(row["sex"]=='female'and row["age"]=='0s'):
            cout1=cout1+1
        if(row["sex"]=='female' and row["age"]=='10s'):
            cout2=cout2+1
        if(row["sex"]=='female' and row["age"]=='20s'):
            cout3=cout3+1
        if(row["sex"]=='female' and row["age"]=='30s'):
            cout4=cout4+1
        if(row["sex"]=='female' and row["age"]=='40s'):
            cout5=cout5+1
        if(row["sex"]=='female' and row["age"]=='50s'):
            cout6=cout6+1
        if(row["sex"]=='female' and row["age"]=='60s'):
            cout7=cout7+1
        if(row["sex"]=='female' and row["age"]=='70s'):
            cout9=cout9+1
        if(row["sex"]=='female' and row["age"]=='80s'):
            cout10=cout10+1
        if(row["sex"]=='female' and row["age"]=='90s'):
            cout11=cout11+1
        if(row["sex"]=='female' and row["age"]=='100s'):
            cout12=cout12+1


print(cout1)
print(cout2)
print(cout3)
print(cout4)
print(cout5)
print(cout6)
print(cout7)
print(cout9)
print(cout10)
print(cout11)
print(cout12)

16
55
379
192
264
343
201
117
102
30
1
In [21]:
plt.figure(figsize=(10,8))
sex2=['male','female','male','female','male','female','male','female','male','female','male','female','male','female','male','female','male','female','male','female','male','female']
age2=['0s','0s','10s','10s','20s','20s','30s','30s','40s','40s','50s','50s','60s','60s','70s','70s','80s','80s','90s','90s','100s','100s']
num=[27,16,71,55,342,379,197,192,168,264,219,343,165,201,72,117,47,102,14,30,0,1]
sns.barplot(x=age2,y=num,hue=sex2)
plt.xlabel('age')
plt.ylabel('patient_number')
Out[21]:
Text(0, 0.5, 'patient_number')

By the picture above,there are more female patients than male patients in most age groups, which is why there are more female patients than male patients overall.

Count the number of patients in different cities

In [22]:
pd.set_option('display.max_columns', None)
from IPython.display import display
pd.set_option('max_columns',1000)
pd.set_option('max_row',300)
display(dataset['city'].value_counts())

Gyeongsan-si       628
Seongnam-si        118
Cheonan-si         106
Bonghwa-gun         71
Bucheon-si          70
Gumi-si             67
Yongin-si           59
Gangnam-gu          53
Pohang-si           50
Chilgok-gun         50
Andong-si           50
Suwon-si            49
Gyeongju-si         48
Sejong              46
Cheongdo-gun        43
Uiseong-gun         41
etc                 41
Gwanak-gu           41
Pyeongtaek-si       37
Yeongcheon-si       36
Songpa-gu           36
Guro-gu             34
Dongnae-gu          33
Seo-gu              31
Seocho-gu           30
Dongdaemun-gu       30
Gunpo-si            29
Changwon-si         29
Nam-gu              28
Dongjak-gu          28
Gangseo-gu          27
Uijeongbu-si        27
Eunpyeong-gu        26
Gimcheon-si         25
Goyang-si           25
Nowon-gu            23
Namyangju-si        23
Jung-gu             22
Seongju-gun         22
Yeongdeungpo-gu     22
Yangcheon-gu        21
Haeundae-gu         20
Hwaseong-si         20
Geochang-gun        19
Wonju-si            19
Mapo-gu             19
Yuseong-gu          19
Seongbuk-gu         18
Anyang-si           18
Yeonsu-gu           18
Seodaemun-gu        18
Yongsan-gu          17
Jungnang-gu         16
Jongno-gu           16
Ansan-si            16
Bupyeong-gu         15
Sangju-si           15
Gimpo-si            13
Busanjin-gu         13
Gwangju-si          13
Gangdong-gu         13
Geumcheon-gu        13
Gwangmyeong-si      12
Dong-gu             12
Cheongju-si         12
Goesan-gun          11
Chungju-si          11
Michuhol-gu         11
Buyeo-gun           11
Pocheon-si          11
Asan-si             10
Namdong-gu          10
Icheon-si           10
Siheung-si          10
Buk-gu              10
Gimhae-si           10
Saha-gu             10
Suyeong-gu           9
Goryeong-gun         9
Changnyeong-gun      9
Jinju-si             9
Seosan-si            8
Jeju-do              8
Hapcheon-gun         8
Seongdong-gu         8
Jeonju-si            7
Gwangjin-gu          7
Osan-si              7
Gangneung-si         7
Dobong-gu            7
Gyeyang-gu           7
Gwacheon-si          6
Geoje-si             6
Gunwi-gun            6
Eumseong-gun         6
Paju-si              6
Geumjeong-gu         6
Yecheon-gun          6
Miryang-si           5
Gangbuk-gu           5
Chuncheon-si         5
Yeongju-si           5
Mokpo-si             5
Uiwang-si            5
Yangsan-si           4
Yeonje-gu            4
Mungyeong-si         4
Guri-si              4
Dalseo-gu            4
Dongducheon-si       4
Yeosu-si             3
Gunsan-si            3
Hanam-si             3
Ulju-gun             3
Daedeok-gu           3
Sokcho-si            3
Goseong-gun          2
Suncheon-si          2
Sasang-gu            2
Anseong-si           2
Yangju-si            2
Cheongsong-gun       2
Hongseong-gun        2
Sacheon-si           2
Yeongyang-gun        2
Yeongdeok-gun        2
Jeungpyeong-gun      2
Muan-gun             2
Suseong-gu           2
Haman-gun            1
Gimje-si             1
Gapyeong-gun         1
Jincheon-gun         1
Hwasun-gun           1
Danyang-gun          1
Inje-gun             1
Samcheok-si          1
Gijang-gun           1
Hamyang-gun          1
Taean-gun            1
Sancheong-gun        1
Iksan-si             1
Namhae-gun           1
Gwangyang-si         1
Gyeryong-si          1
Taebaek-si           1
Dalseong-gun         1
Name: city, dtype: int64

I just take top 5 cities to visualize

In [23]:
plt.figure(figsize=(10,8))
pro1=['Gyeongsan-si','Seonganam-si','Cheonan-si','Bonghwa-gun','Bucheon-si']
numm1=[628,118,106,71,70]
sns.barplot(x=pro1,y=numm1)
plt.xlabel('City')
plt.ylabel('Patient_number')
Out[23]:
Text(0, 0.5, 'Patient_number')

Count the number of patients in different provinces

In [24]:
dataset['province'].value_counts()
Out[24]:
Gyeongsangbuk-do     1182
Gyeonggi-do           601
Seoul                 560
Chungcheongnam-do     138
Busan                 122
Gyeongsangnam-do      110
Incheon                80
Daegu                  63
Sejong                 46
Chungcheongbuk-do      44
Ulsan                  40
Daejeon                39
Gangwon-do             37
Gwangju                27
Jeollabuk-do           15
Jeollanam-do           15
Jeju-do                 9
Name: province, dtype: int64
In [25]:
plt.figure(figsize=(26,15))
pro=['Gyeongsangbuk-do','Gyeonggi-do','Seoul','Chungcheongnam-do','Busan','Gyeongsangnam-do','Incheon','Daegu','Sejong','Chungcheongbuk-do','Ulsan','Daejeon','Gangwon-do','Gwangju','Jeollabuk-do','Jeollanam-do','Jeju-do']
numm=[1182,601,560,138,122,110,80,63,46,44,40,39,37,27,15,15,9]
sns.barplot(x=pro,y=numm)
plt.xlabel('Province')
plt.ylabel('Patient_number')
Out[25]:
Text(0, 0.5, 'Patient_number')

By the picture above,there are three main provinces whose patients are much more than other province.

Count the patients of different infectino_case and state

In [26]:
dataset['infection_case'].value_counts()
Out[26]:
contact with patient                     862
etc                                      499
overseas inflow                          475
Guro-gu Call Center                      112
Shincheonji Church                       105
Onchun Church                             33
Bonghwa Pureun Nursing Home               31
gym facility in Cheonan                   30
Ministry of Oceans and Fisheries          28
Cheongdo Daenam Hospital                  21
Dongan Church                             17
Eunpyeong St. Mary's Hospital             16
Gyeongsan Seorin Nursing Home             14
Seongdong-gu APT                          13
Gyeongsan Jeil Silver Town                12
Milal Shelter                             11
Gyeongsan Cham Joeun Community Center     10
Geochang Church                            6
Changnyeong Coin Karaoke                   4
gym facility in Sejong                     4
Suyeong-gu Kindergarten                    3
Pilgrimage to Israel                       2
River of Grace Community Church            1
Name: infection_case, dtype: int64
In [27]:
dataset['state'].value_counts()
Out[27]:
isolated    1934
released    1133
deceased      61
Name: state, dtype: int64
In [28]:
dataset['infection_case'].value_counts()
Out[28]:
contact with patient                     862
etc                                      499
overseas inflow                          475
Guro-gu Call Center                      112
Shincheonji Church                       105
Onchun Church                             33
Bonghwa Pureun Nursing Home               31
gym facility in Cheonan                   30
Ministry of Oceans and Fisheries          28
Cheongdo Daenam Hospital                  21
Dongan Church                             17
Eunpyeong St. Mary's Hospital             16
Gyeongsan Seorin Nursing Home             14
Seongdong-gu APT                          13
Gyeongsan Jeil Silver Town                12
Milal Shelter                             11
Gyeongsan Cham Joeun Community Center     10
Geochang Church                            6
Changnyeong Coin Karaoke                   4
gym facility in Sejong                     4
Suyeong-gu Kindergarten                    3
Pilgrimage to Israel                       2
River of Grace Community Church            1
Name: infection_case, dtype: int64

Visulize the infection_case

In [29]:
plt.figure(figsize=(10,8))
pro1=['contact','overseas','Guro-gu Call Center','Shinchenonji Church','etc']
numm1=[862,475,112,105,755]
sns.barplot(x=pro1,y=numm1)
plt.xlabel('Infection_case')
plt.ylabel('Patient_number')
Out[29]:
Text(0, 0.5, 'Patient_number')

By the picture above,contacting is still the worse case to spread virus.Besides,overseas spreading is also a big transmission route of the virus.In Guro-gu Call Center and Shinchenonji Church,There was a cluster of infections.There factors are necessary to take seriously.

Visulize the state

In [30]:
plt.figure(figsize=(3,8))
pro=['isolated','released','deceased']
numm=[1934,1133,61]
sns.barplot(x=pro,y=numm)
plt.xlabel('State')
plt.ylabel('Patient_number')
Out[30]:
Text(0, 0.5, 'Patient_number')

By the picture above,most patients are still isolated and quite a few patients are released.Only few patients are deceased.

Count the number of patients in different state and sex

In [31]:
import csv
cout1=0
cout2=0
cout3=0
cout4=0
cout5=0
cout6=0
cout7=0
cout8=0
cout9=0
cout10=0
cout11=0
cout12=0
with open(r"C:\Users\hp\Desktop\PatientInfo.csv",'r') as csvfile:
    reader = csv.DictReader(csvfile)
    for row in reader:
        if(row["sex"]=='female'and row["state"]=='isolated'):
            cout1=cout1+1
        if(row["sex"]=='female' and row["state"]=='released'):
            cout2=cout2+1
        if(row["sex"]=='female' and row["state"]=='deceased'):
            cout3=cout3+1
        if(row["sex"]=='male' and row["state"]=='isolated'):
            cout4=cout4+1
        if(row["sex"]=='male' and row["state"]=='released'):
            cout5=cout5+1
        if(row["sex"]=='male' and row["state"]=='deceased'):
            cout6=cout6+1


print(cout1)
print(cout2)
print(cout3)
print(cout4)
print(cout5)
print(cout6)

1028
659
20
814
472
41

Visulize the number of patients in different sex and state

In [32]:
plt.figure(figsize=(8,10))
sex2=['male','female','male','female','male','female']
age2=['isolated','isolated','released','released','deceased','deceased']
num=[814,1028,472,659,41,20]
sns.barplot(x=age2,y=num,hue=sex2)
plt.xlabel('State')
plt.ylabel('Patient_number')
Out[32]:
Text(0, 0.5, 'Patient_number')

As expected,fenale patients are more than male patients in isolated and released.Only deceased male patients are little more than female patients.

Deal with and Visuliaze Time.csv

Count the number of different state

In [33]:
import csv

with open(r"C:\Users\hp\Desktop\Time.csv",'r') as csvfile:
    reader = csv.DictReader(csvfile)
    test = [int(row['test']) for row in reader]

print (test)

[1, 1, 4, 22, 27, 27, 51, 61, 116, 187, 246, 312, 371, 429, 490, 607, 714, 885, 1352, 2097, 2598, 3110, 4325, 5624, 6511, 7242, 7734, 8161, 8718, 9772, 11173, 13202, 16400, 21586, 26179, 32756, 40304, 53553, 66652, 81167, 94055, 98921, 109591, 125851, 136707, 146541, 164740, 178189, 188518, 196618, 210144, 222395, 234998, 248647, 261335, 268212, 274504, 286716, 295647, 307024, 316664, 327509, 331780, 338036, 348582, 357896, 364942, 376961, 387925, 394141, 395194, 410564, 421547, 431743, 443273, 455032, 461233, 466804, 477304]
In [34]:
import csv

with open(r"C:\Users\hp\Desktop\Time.csv",'r') as csvfile:
    reader = csv.DictReader(csvfile)
    confirmed = [int(row['confirmed']) for row in reader]

print (confirmed)

[1, 1, 1, 1, 2, 2, 3, 4, 4, 4, 6, 11, 12, 15, 15, 16, 18, 23, 24, 24, 27, 27, 28, 28, 28, 28, 28, 29, 30, 31, 51, 104, 204, 433, 602, 833, 977, 1261, 1766, 2337, 3150, 3736, 4212, 4812, 5328, 5766, 6284, 6767, 7134, 7382, 7513, 7755, 7869, 7979, 8086, 8126, 8236, 8320, 8413, 8565, 8652, 8799, 8897, 8961, 9037, 9137, 9241, 9332, 9478, 9583, 9661, 9786, 9887, 9976, 10062, 10156, 10237, 10284, 10331]
In [35]:
import csv

with open(r"C:\Users\hp\Desktop\Time.csv",'r') as csvfile:
    reader = csv.DictReader(csvfile)
    negative = [int(row['negative']) for row in reader]

print (negative)

[0, 0, 3, 21, 25, 25, 47, 56, 97, 155, 199, 245, 289, 327, 414, 462, 522, 693, 1001, 1134, 1683, 2552, 3535, 4811, 5921, 6679, 7148, 7647, 7980, 8923, 9973, 11238, 13016, 15116, 17520, 20292, 25447, 31576, 39318, 48593, 55723, 61825, 71580, 85484, 102965, 118965, 136624, 151802, 162008, 171778, 184179, 196100, 209402, 222728, 235615, 243778, 251297, 261105, 270888, 282555, 292487, 303006, 308343, 315447, 324105, 334481, 341332, 352410, 361883, 369530, 372002, 383886, 395075, 403882, 414303, 424732, 431425, 437225, 446323]

Visulize confirmed patients and time

In [36]:
import matplotlib as mat
import datetime
f=plt.figure(figsize=(15,8))
confirmed=[1, 1, 1, 1, 2, 2, 3, 4, 4, 4, 6, 11, 12, 15, 15, 16, 18, 23, 24, 24, 27, 27, 28, 28, 28, 28, 28, 29, 30, 31, 51, 104, 204, 433, 602, 833, 977, 1261, 1766, 2337, 3150, 3736, 4212, 4812, 5328, 5766, 6284, 6767, 7134, 7382, 7513, 7755, 7869, 7979, 8086, 8126, 8236, 8320, 8413, 8565, 8652, 8799, 8897, 8961, 9037, 9137, 9241, 9332, 9478, 9583, 9661, 9786, 9887, 9976, 10062, 10156, 10237, 10284, 10331]
test=[1, 1, 4, 22, 27, 27, 51, 61, 116, 187, 246, 312, 371, 429, 490, 607, 714, 885, 1352, 2097, 2598, 3110, 4325, 5624, 6511, 7242, 7734, 8161, 8718, 9772, 11173, 13202, 16400, 21586, 26179, 32756, 40304, 53553, 66652, 81167, 94055, 98921, 109591, 125851, 136707, 146541, 164740, 178189, 188518, 196618, 210144, 222395, 234998, 248647, 261335, 268212, 274504, 286716, 295647, 307024, 316664, 327509, 331780, 338036, 348582, 357896, 364942, 376961, 387925, 394141, 395194, 410564, 421547, 431743, 443273, 455032, 461233, 466804, 477304]
negative=[0, 0, 3, 21, 25, 25, 47, 56, 97, 155, 199, 245, 289, 327, 414, 462, 522, 693, 1001, 1134, 1683, 2552, 3535, 4811, 5921, 6679, 7148, 7647, 7980, 8923, 9973, 11238, 13016, 15116, 17520, 20292, 25447, 31576, 39318, 48593, 55723, 61825, 71580, 85484, 102965, 118965, 136624, 151802, 162008, 171778, 184179, 196100, 209402, 222728, 235615, 243778, 251297, 261105, 270888, 282555, 292487, 303006, 308343, 315447, 324105, 334481, 341332, 352410, 361883, 369530, 372002, 383886, 395075, 403882, 414303, 424732, 431425, 437225, 446323]
std=datetime.datetime(2020,1,19)
edd=datetime.datetime(2020,4,7)
jida=datetime.timedelta(days=1)
dates=mat.dates.drange(std,edd,jida)
axis=plt.gca()
dateFormat=mat.dates.DateFormatter('%Y-%m-%d')
axis.xaxis.set_major_formatter(dateFormat)
axis.plot(dates,confirmed)
plt.xlabel('Date')
plt.ylabel('Patient_number')
legend=plt.legend(["confirmed"])
In [37]:
import pandas as pd
import numpy as np
datase = pd.read_csv(r"C:\Users\hp\Desktop\Time.csv")

Visulize three different state with time

In [38]:
import matplotlib as mat
import datetime
f=plt.figure(figsize=(15,8))
confirmed=[1, 1, 1, 1, 2, 2, 3, 4, 4, 4, 6, 11, 12, 15, 15, 16, 18, 23, 24, 24, 27, 27, 28, 28, 28, 28, 28, 29, 30, 31, 51, 104, 204, 433, 602, 833, 977, 1261, 1766, 2337, 3150, 3736, 4212, 4812, 5328, 5766, 6284, 6767, 7134, 7382, 7513, 7755, 7869, 7979, 8086, 8126, 8236, 8320, 8413, 8565, 8652, 8799, 8897, 8961, 9037, 9137, 9241, 9332, 9478, 9583, 9661, 9786, 9887, 9976, 10062, 10156, 10237, 10284, 10331]
test=[1, 1, 4, 22, 27, 27, 51, 61, 116, 187, 246, 312, 371, 429, 490, 607, 714, 885, 1352, 2097, 2598, 3110, 4325, 5624, 6511, 7242, 7734, 8161, 8718, 9772, 11173, 13202, 16400, 21586, 26179, 32756, 40304, 53553, 66652, 81167, 94055, 98921, 109591, 125851, 136707, 146541, 164740, 178189, 188518, 196618, 210144, 222395, 234998, 248647, 261335, 268212, 274504, 286716, 295647, 307024, 316664, 327509, 331780, 338036, 348582, 357896, 364942, 376961, 387925, 394141, 395194, 410564, 421547, 431743, 443273, 455032, 461233, 466804, 477304]
negative=[0, 0, 3, 21, 25, 25, 47, 56, 97, 155, 199, 245, 289, 327, 414, 462, 522, 693, 1001, 1134, 1683, 2552, 3535, 4811, 5921, 6679, 7148, 7647, 7980, 8923, 9973, 11238, 13016, 15116, 17520, 20292, 25447, 31576, 39318, 48593, 55723, 61825, 71580, 85484, 102965, 118965, 136624, 151802, 162008, 171778, 184179, 196100, 209402, 222728, 235615, 243778, 251297, 261105, 270888, 282555, 292487, 303006, 308343, 315447, 324105, 334481, 341332, 352410, 361883, 369530, 372002, 383886, 395075, 403882, 414303, 424732, 431425, 437225, 446323]
axis=plt.gca()
starttime=datetime.datetime(2020,1,19)
endtime=datetime.datetime(2020,4,7)
jianju=datetime.timedelta(days=1)
daes=mat.dates.drange(starttime,endtime,jianju)
axis=plt.gca()
dateFormat=mat.dates.DateFormatter('%Y-%m-%d')
axis.xaxis.set_major_formatter(dateFormat)
axis.plot(daes,test)
axis.plot(daes,negative)
axis.plot(daes,confirmed)
legend=plt.legend(["test","negative","confirmed"])
plt.xlabel('Date')
plt.ylabel('Patient_number')
Out[38]:
Text(0, 0.5, 'Patient_number')

Around February 18, 2020,test and negative patients begin inflection point and have rapid growth.

Deal with and visulize SearchTrend.csv

Since the outbreak probably occurred on January 16, 2020,I take information from January 16,2020 and create a new document called Search.csv

Count the different number of states

In [39]:
import pandas as pd
import numpy as np
datase = pd.read_csv(r"C:\Users\hp\Desktop\Search.csv")
In [40]:
import csv

with open(r"C:\Users\hp\Desktop\Search.csv",'r') as csvfile:
    reader = csv.DictReader(csvfile)
    date = [float(row['cold']) for row in reader]

print (date)

[0.19217, 0.22462, 0.23808, 0.30308, 0.34689, 0.70888, 0.96569, 1.39513, 1.56922, 1.19978, 0.98978, 1.35841, 1.29296, 1.4925, 1.20932, 1.06169, 1.09323, 1.03724, 0.91715, 0.89597, 0.77052, 0.63816, 0.5338, 0.47862, 0.38444, 0.32153, 0.35944, 0.35171, 0.32426, 0.32108, 0.59207, 0.98306, 1.42804, 1.91594, 1.94203, 1.78603, 1.67058, 1.42177, 1.2255, 1.24941, 1.3085, 1.23605, 0.8816, 0.67934, 0.82579, 0.64916, 0.58625, 0.71743, 0.58752, 0.45435, 0.46934, 0.70143, 0.55771, 0.40744, 0.50816, 5.0273, 0.52925, 0.49134, 0.44625, 0.47171, 0.44453, 0.4738, 0.94142, 0.36689, 0.29562, 0.29508, 0.27471, 0.28517, 0.32717, 0.32462, 0.31962, 0.30762, 0.2289, 0.22862, 0.22453, 0.26708, 0.26281, 0.20717]
In [41]:
import csv

with open(r"C:\Users\hp\Desktop\Search.csv",'r') as csvfile:
    reader = csv.DictReader(csvfile)
    date = [float(row['flu']) for row in reader]

print (date)

[0.70343, 0.59789, 0.56661, 0.55625, 0.40226, 0.39744, 0.40126, 0.50253, 0.64298, 0.49207, 0.44953, 0.84061, 0.79288, 0.45298, 0.49516, 0.37907, 0.34444, 0.31644, 0.25808, 0.18081, 0.17208, 0.21499, 0.17072, 0.15281, 0.13999, 0.11808, 0.10026, 0.0899, 0.12045, 0.11963, 0.13172, 0.17026, 0.17808, 0.19472, 0.19326, 0.22708, 0.20181, 0.19281, 0.18535, 0.16544, 0.16108, 0.13754, 0.13345, 0.11917, 0.1079, 0.09745, 0.09163, 0.0769, 0.06717, 0.09263, 0.09108, 0.08945, 0.10008, 0.07808, 0.0689, 0.06754, 0.12036, 0.2749, 0.15999, 0.10754, 0.14426, 0.09381, 0.07108, 0.09354, 0.0739, 0.06099, 0.06699, 0.07836, 0.06272, 0.06754, 0.06354, 0.05954, 0.06018, 0.05527, 0.05118, 0.0549, 0.04918, 0.05763]
In [42]:
import csv

with open(r"C:\Users\hp\Desktop\Search.csv",'r') as csvfile:
    reader = csv.DictReader(csvfile)
    date = [float(row['pneumonia']) for row in reader]

print (date)

[3.63716, 4.31987, 3.66416, 3.18035, 2.48156, 3.40926, 3.43989, 4.38777, 4.5434, 2.73, 2.17811, 2.1802, 1.44032, 1.37923, 1.33886, 0.99397, 0.85588, 0.79524, 0.80733, 0.53789, 0.46189, 0.50871, 0.40544, 0.37889, 0.37753, 0.2769, 0.25471, 0.35953, 0.45144, 1.00833, 0.96769, 1.15151, 1.17051, 1.27032, 1.15841, 1.17887, 1.02206, 0.87142, 0.75252, 0.6417, 0.52989, 0.44589, 0.4408, 0.39853, 0.35671, 0.31726, 0.28071, 0.22762, 0.20553, 0.23962, 0.28635, 0.30208, 0.26335, 0.22799, 0.19108, 0.16644, 0.20826, 0.24172, 0.56771, 1.10896, 0.88197, 0.28426, 0.20372, 0.24172, 0.19799, 0.18535, 0.16899, 0.17272, 0.1399, 0.12863, 0.1789, 0.18817, 0.1709, 0.16108, 0.15517, 0.1209, 0.11436, 0.14026]
In [43]:
import csv

with open(r"C:\Users\hp\Desktop\Search.csv",'r') as csvfile:
    reader = csv.DictReader(csvfile)
    date = [float(row['coronavirus']) for row in reader]

print (date)

[20.6961, 35.33284, 29.74474, 100.0, 86.11541, 62.84847, 39.62772, 57.07446, 72.63073, 52.06261, 45.93889, 58.4187, 41.5073, 41.73647, 50.1994, 37.28806, 35.23094, 39.3959, 31.71404, 40.46614, 36.07691, 52.11234, 18.13918, 15.90997, 15.08472, 16.94803, 10.34468, 9.79906, 10.98521, 14.97945, 40.75986, 59.21885, 72.15665, 85.23907, 82.90014, 79.89478, 79.95441, 71.26268, 71.07204, 66.20619, 61.04053, 56.20422, 60.86853, 47.17367, 39.84053, 33.24218, 29.61647, 26.90492, 26.4902, 29.79228, 28.49514, 24.02346, 20.56511, 19.29851, 16.62577, 14.92164, 16.8914, 14.70828, 13.9044, 18.50553, 13.56359, 11.28111, 11.1433, 11.56656, 10.87394, 10.51795, 10.38105, 10.15869, 10.25105, 8.86464, 8.6911, 8.30075, 7.70922, 7.50622, 7.44741, 6.04172, 5.55301, 10.90885]
In [44]:
import matplotlib as mat
import datetime
f=plt.figure(figsize=(15,8))
cold=[0.14345,0.15217,0.19217, 0.22462, 0.23808, 0.30308, 0.34689, 0.70888, 0.96569, 1.39513, 1.56922, 1.19978, 0.98978, 1.35841, 1.29296, 1.4925, 1.20932, 1.06169, 1.09323, 1.03724, 0.91715, 0.89597, 0.77052, 0.63816, 0.5338, 0.47862, 0.38444, 0.32153, 0.35944, 0.35171, 0.32426, 0.32108, 0.59207, 0.98306, 1.42804, 1.91594, 1.94203, 1.78603, 1.67058, 1.42177, 1.2255, 1.24941, 1.3085, 1.23605, 0.8816, 0.67934, 0.82579, 0.64916, 0.58625, 0.71743, 0.58752, 0.45435, 0.46934, 0.70143, 0.55771, 0.40744, 0.50816, 5.0273, 0.52925, 0.49134, 0.44625, 0.47171, 0.44453, 0.4738, 0.94142, 0.36689, 0.29562, 0.29508, 0.27471, 0.28517, 0.32717, 0.32462, 0.31962, 0.30762, 0.2289, 0.22862, 0.22453, 0.26708, 0.26281, 0.20717]
flu=[0.42089,0.39953,0.70343, 0.59789, 0.56661, 0.55625, 0.40226, 0.39744, 0.40126, 0.50253, 0.64298, 0.49207, 0.44953, 0.84061, 0.79288, 0.45298, 0.49516, 0.37907, 0.34444, 0.31644, 0.25808, 0.18081, 0.17208, 0.21499, 0.17072, 0.15281, 0.13999, 0.11808, 0.10026, 0.0899, 0.12045, 0.11963, 0.13172, 0.17026, 0.17808, 0.19472, 0.19326, 0.22708, 0.20181, 0.19281, 0.18535, 0.16544, 0.16108, 0.13754, 0.13345, 0.11917, 0.1079, 0.09745, 0.09163, 0.0769, 0.06717, 0.09263, 0.09108, 0.08945, 0.10008, 0.07808, 0.0689, 0.06754, 0.12036, 0.2749, 0.15999, 0.10754, 0.14426, 0.09381, 0.07108, 0.09354, 0.0739, 0.06099, 0.06699, 0.07836, 0.06272, 0.06754, 0.06354, 0.05954, 0.06018, 0.05527, 0.05118, 0.0549, 0.04918, 0.05763]
pne=[0.55216,0.61598,3.63716, 4.31987, 3.66416, 3.18035, 2.48156, 3.40926, 3.43989, 4.38777, 4.5434, 2.73, 2.17811, 2.1802, 1.44032, 1.37923, 1.33886, 0.99397, 0.85588, 0.79524, 0.80733, 0.53789, 0.46189, 0.50871, 0.40544, 0.37889, 0.37753, 0.2769, 0.25471, 0.35953, 0.45144, 1.00833, 0.96769, 1.15151, 1.17051, 1.27032, 1.15841, 1.17887, 1.02206, 0.87142, 0.75252, 0.6417, 0.52989, 0.44589, 0.4408, 0.39853, 0.35671, 0.31726, 0.28071, 0.22762, 0.20553, 0.23962, 0.28635, 0.30208, 0.26335, 0.22799, 0.19108, 0.16644, 0.20826, 0.24172, 0.56771, 1.10896, 0.88197, 0.28426, 0.20372, 0.24172, 0.19799, 0.18535, 0.16899, 0.17272, 0.1399, 0.12863, 0.1789, 0.18817, 0.1709, 0.16108, 0.15517, 0.1209, 0.11436, 0.14026]
cor=[0.26262,0.32662,20.6961, 35.33284, 29.74474, 100.0, 86.11541, 62.84847, 39.62772, 57.07446, 72.63073, 52.06261, 45.93889, 58.4187, 41.5073, 41.73647, 50.1994, 37.28806, 35.23094, 39.3959, 31.71404, 40.46614, 36.07691, 52.11234, 18.13918, 15.90997, 15.08472, 16.94803, 10.34468, 9.79906, 10.98521, 14.97945, 40.75986, 59.21885, 72.15665, 85.23907, 82.90014, 79.89478, 79.95441, 71.26268, 71.07204, 66.20619, 61.04053, 56.20422, 60.86853, 47.17367, 39.84053, 33.24218, 29.61647, 26.90492, 26.4902, 29.79228, 28.49514, 24.02346, 20.56511, 19.29851, 16.62577, 14.92164, 16.8914, 14.70828, 13.9044, 18.50553, 13.56359, 11.28111, 11.1433, 11.56656, 10.87394, 10.51795, 10.38105, 10.15869, 10.25105, 8.86464, 8.6911, 8.30075, 7.70922, 7.50622, 7.44741, 6.04172, 5.55301, 10.90885]
axis=plt.gca()
starttime=datetime.datetime(2020,1,17)
endtime=datetime.datetime(2020,4,6)
jianju=datetime.timedelta(days=1)
daes=mat.dates.drange(starttime,endtime,jianju)
axis=plt.gca()
dateFormat=mat.dates.DateFormatter('%Y-%m-%d')
axis.xaxis.set_major_formatter(dateFormat)
axis.plot(daes,cold)
axis.plot(daes,flu)
axis.plot(daes,pne)
axis.plot(daes,cor)
legend=plt.legend(["cold","flu","pneumonia","coronavirus"])
plt.xlabel('Date')
plt.ylabel('Search_trend')
Out[44]:
Text(0, 0.5, 'Search_trend')

We can see the search trend of different state.Paticularly,'coronavirus' is a most important part in people's eyes.

Count the number of patients in different age and state is 'confirmed' in TimeAge.csv

In [45]:
import csv
cout1=[]
cout2=[]
cout3=[]
cout4=[]
cout5=[]
cout6=[]
cout7=[]
cout8=[]
cout9=[]
cout10=0
cout11=0
cout12=0
with open(r"C:\Users\hp\Desktop\TimeAge.csv",'r') as csvfile:
    reader = csv.DictReader(csvfile)
    for row in reader:
        if(row["age"]=='0s'):
            cout1.append(int(row["confirmed"]))
        if(row["age"]=='10s'):
            cout2.append(int(row["confirmed"]))
        if(row["age"]=='20s'):
            cout3.append(int(row["confirmed"]))
        if(row["age"]=='30s'):
            cout4.append(int(row["confirmed"]))
        if(row["age"]=='40s'):
            cout5.append(int(row["confirmed"]))
        if(row["age"]=='50s'):
            cout6.append(int(row["confirmed"]))
        if(row["age"]=='60s'):
            cout7.append(int(row["confirmed"]))
        if(row["age"]=='70s'):
            cout8.append(int(row["confirmed"]))
        if(row["age"]=='80s'):
            cout9.append(int(row["confirmed"]))

print(cout1)
print(len(cout1))
print(cout2)
print(len(cout2))
print(cout3)
print(len(cout3))
print(cout4)
print(len(cout4))
print(cout5)
print(len(cout5))
print(cout6)
print(len(cout6))
print(cout7)
print(len(cout7))
print(cout8)
print(len(cout8))
print(cout9)
print(len(cout9))

[32, 34, 34, 38, 45, 52, 58, 66, 67, 75, 76, 77, 81, 83, 85, 86, 87, 91, 97, 99, 101, 103, 105, 105, 106, 108, 109, 111, 112, 112, 116, 119, 121, 122, 124, 126, 126]
37
[169, 204, 233, 257, 292, 327, 360, 381, 393, 405, 412, 421, 424, 427, 432, 436, 438, 444, 452, 457, 460, 460, 468, 475, 488, 496, 501, 508, 513, 515, 519, 522, 528, 530, 535, 542, 544]
37
[1235, 1417, 1575, 1727, 1877, 2038, 2133, 2190, 2213, 2238, 2261, 2274, 2287, 2301, 2313, 2330, 2342, 2358, 2365, 2380, 2396, 2417, 2438, 2473, 2508, 2532, 2567, 2602, 2630, 2656, 2682, 2704, 2734, 2761, 2789, 2804, 2819]
37
[506, 578, 631, 659, 693, 727, 760, 779, 789, 804, 812, 823, 833, 842, 849, 856, 873, 886, 893, 900, 909, 917, 921, 943, 955, 960, 978, 993, 1002, 1012, 1027, 1043, 1052, 1066, 1083, 1086, 1092]
37
[633, 713, 790, 847, 889, 941, 975, 1005, 1030, 1082, 1101, 1117, 1133, 1141, 1147, 1164, 1171, 1181, 1193, 1203, 1221, 1228, 1234, 1246, 1252, 1256, 1278, 1292, 1297, 1312, 1323, 1336, 1350, 1358, 1370, 1375, 1382]
37
[834, 952, 1051, 1127, 1217, 1287, 1349, 1391, 1416, 1472, 1495, 1523, 1551, 1568, 1585, 1602, 1615, 1642, 1656, 1672, 1691, 1702, 1716, 1724, 1738, 1752, 1780, 1798, 1812, 1851, 1865, 1878, 1887, 1898, 1904, 1906, 1909]
37
[530, 597, 646, 699, 763, 830, 878, 916, 929, 960, 972, 985, 999, 1012, 1024, 1033, 1059, 1080, 1099, 1118, 1132, 1139, 1146, 1154, 1162, 1170, 1201, 1210, 1218, 1235, 1245, 1258, 1266, 1282, 1289, 1294, 1304]
37
[192, 224, 260, 288, 340, 384, 409, 438, 454, 483, 497, 506, 515, 525, 531, 539, 542, 562, 568, 589, 595, 599, 608, 611, 616, 630, 632, 635, 640, 651, 658, 663, 668, 678, 681, 686, 689]
37
[81, 93, 108, 124, 168, 191, 212, 216, 222, 236, 243, 253, 263, 263, 270, 274, 286, 321, 329, 381, 392, 396, 401, 406, 416, 428, 432, 434, 437, 442, 452, 453, 456, 461, 462, 465, 466]
37

Visualision

In [46]:
import matplotlib as mat
import datetime
f=plt.figure(figsize=(15,8))
axis.xaxis.set_major_formatter(dateFormat)
confirmed=[1, 1, 1, 1, 2, 2, 3, 4, 4, 4, 6, 11, 12, 15, 15, 16, 18, 23, 24, 24, 27, 27, 28, 28, 28, 28, 28, 29, 30, 31, 51, 104, 204, 433, 602, 833, 977, 1261, 1766, 2337, 3150, 3736, 4212, 4812, 5328, 5766, 6284, 6767, 7134, 7382, 7513, 7755, 7869, 7979, 8086, 8126, 8236, 8320, 8413, 8565, 8652, 8799, 8897, 8961, 9037, 9137, 9241, 9332, 9478, 9583, 9661, 9786, 9887, 9976, 10062, 10156, 10237, 10284, 10331]
test=[1, 1, 4, 22, 27, 27, 51, 61, 116, 187, 246, 312, 371, 429, 490, 607, 714, 885, 1352, 2097, 2598, 3110, 4325, 5624, 6511, 7242, 7734, 8161, 8718, 9772, 11173, 13202, 16400, 21586, 26179, 32756, 40304, 53553, 66652, 81167, 94055, 98921, 109591, 125851, 136707, 146541, 164740, 178189, 188518, 196618, 210144, 222395, 234998, 248647, 261335, 268212, 274504, 286716, 295647, 307024, 316664, 327509, 331780, 338036, 348582, 357896, 364942, 376961, 387925, 394141, 395194, 410564, 421547, 431743, 443273, 455032, 461233, 466804, 477304]
negative=[0, 0, 3, 21, 25, 25, 47, 56, 97, 155, 199, 245, 289, 327, 414, 462, 522, 693, 1001, 1134, 1683, 2552, 3535, 4811, 5921, 6679, 7148, 7647, 7980, 8923, 9973, 11238, 13016, 15116, 17520, 20292, 25447, 31576, 39318, 48593, 55723, 61825, 71580, 85484, 102965, 118965, 136624, 151802, 162008, 171778, 184179, 196100, 209402, 222728, 235615, 243778, 251297, 261105, 270888, 282555, 292487, 303006, 308343, 315447, 324105, 334481, 341332, 352410, 361883, 369530, 372002, 383886, 395075, 403882, 414303, 424732, 431425, 437225, 446323]
c1=[32, 34, 34, 38, 45, 52, 58, 66, 67, 75, 76, 77, 81, 83, 85, 86, 87, 91, 97, 99, 101, 103, 105, 105, 106, 108, 109, 111, 112, 112, 116, 119, 121, 122, 124, 126, 126]
c2=[169, 204, 233, 257, 292, 327, 360, 381, 393, 405, 412, 421, 424, 427, 432, 436, 438, 444, 452, 457, 460, 460, 468, 475, 488, 496, 501, 508, 513, 515, 519, 522, 528, 530, 535, 542, 544]
c3=[1235, 1417, 1575, 1727, 1877, 2038, 2133, 2190, 2213, 2238, 2261, 2274, 2287, 2301, 2313, 2330, 2342, 2358, 2365, 2380, 2396, 2417, 2438, 2473, 2508, 2532, 2567, 2602, 2630, 2656, 2682, 2704, 2734, 2761, 2789, 2804, 2819]
c4=[506, 578, 631, 659, 693, 727, 760, 779, 789, 804, 812, 823, 833, 842, 849, 856, 873, 886, 893, 900, 909, 917, 921, 943, 955, 960, 978, 993, 1002, 1012, 1027, 1043, 1052, 1066, 1083, 1086, 1092]
c5=[633, 713, 790, 847, 889, 941, 975, 1005, 1030, 1082, 1101, 1117, 1133, 1141, 1147, 1164, 1171, 1181, 1193, 1203, 1221, 1228, 1234, 1246, 1252, 1256, 1278, 1292, 1297, 1312, 1323, 1336, 1350, 1358, 1370, 1375, 1382]
c6=[834, 952, 1051, 1127, 1217, 1287, 1349, 1391, 1416, 1472, 1495, 1523, 1551, 1568, 1585, 1602, 1615, 1642, 1656, 1672, 1691, 1702, 1716, 1724, 1738, 1752, 1780, 1798, 1812, 1851, 1865, 1878, 1887, 1898, 1904, 1906, 1909]
c7=[530, 597, 646, 699, 763, 830, 878, 916, 929, 960, 972, 985, 999, 1012, 1024, 1033, 1059, 1080, 1099, 1118, 1132, 1139, 1146, 1154, 1162, 1170, 1201, 1210, 1218, 1235, 1245, 1258, 1266, 1282, 1289, 1294, 1304]
c8=[192, 224, 260, 288, 340, 384, 409, 438, 454, 483, 497, 506, 515, 525, 531, 539, 542, 562, 568, 589, 595, 599, 608, 611, 616, 630, 632, 635, 640, 651, 658, 663, 668, 678, 681, 686, 689]
c9=[81, 93, 108, 124, 168, 191, 212, 216, 222, 236, 243, 253, 263, 263, 270, 274, 286, 321, 329, 381, 392, 396, 401, 406, 416, 428, 432, 434, 437, 442, 452, 453, 456, 461, 462, 465, 466]

axis=plt.gca()
starttime=datetime.datetime(2020,3,1)
endtime=datetime.datetime(2020,4,7)
jianju=datetime.timedelta(days=1)
daes=mat.dates.drange(starttime,endtime,jianju)
axis=plt.gca()
dateFormat=mat.dates.DateFormatter('%Y-%m-%d')
axis.xaxis.set_major_formatter(dateFormat)
axis.plot(daes,c1)
axis.plot(daes,c2)
axis.plot(daes,c3)
axis.plot(daes,c4)
axis.plot(daes,c5)
axis.plot(daes,c6)
axis.plot(daes,c7)
axis.plot(daes,c8)
axis.plot(daes,c9)
axis.xaxis.set_major_formatter(dateFormat)
legend=plt.legend(["0s","10s","20s","30s","40s","50s","60s","70s","80s"])
plt.xlabel('Date')
plt.ylabel('Different_age_number')
Out[46]:
Text(0, 0.5, 'Different_age_number')

Visulize sex and 'confirmed' in TimeGender.csv

In [47]:
import csv
cout1=[]
cout2=[]
with open(r"C:\Users\hp\Desktop\TimeGender.csv",'r') as csvfile:
    reader = csv.DictReader(csvfile)
    for row in reader:
        if(row["sex"]=='male'):
            cout1.append(int(row["confirmed"]))
        if(row["sex"]=='female'):
            cout2.append(int(row["confirmed"]))

print(cout1)
print(len(cout1))
print(cout2)
print(len(cout2))

[1591, 1810, 1996, 2149, 2345, 2522, 2694, 2799, 2852, 2947, 2994, 3043, 3100, 3136, 3169, 3200, 3240, 3296, 3330, 3387, 3430, 3457, 3497, 3550, 3598, 3638, 3736, 3799, 3834, 3905, 3946, 3979, 4013, 4052, 4098, 4118, 4138]
37
[2621, 3002, 3332, 3617, 3939, 4245, 4440, 4583, 4661, 4808, 4875, 4936, 4986, 5026, 5067, 5120, 5173, 5269, 5322, 5412, 5467, 5504, 5540, 5587, 5643, 5694, 5742, 5784, 5827, 5881, 5941, 5997, 6049, 6104, 6139, 6166, 6193]
37
In [48]:
import matplotlib as mat
import datetime
f=plt.figure(figsize=(15,8))
x1=[1591, 1810, 1996, 2149, 2345, 2522, 2694, 2799, 2852, 2947, 2994, 3043, 3100, 3136, 3169, 3200, 3240, 3296, 3330, 3387, 3430, 3457, 3497, 3550, 3598, 3638, 3736, 3799, 3834, 3905, 3946, 3979, 4013, 4052, 4098, 4118, 4138]
x2=[2621, 3002, 3332, 3617, 3939, 4245, 4440, 4583, 4661, 4808, 4875, 4936, 4986, 5026, 5067, 5120, 5173, 5269, 5322, 5412, 5467, 5504, 5540, 5587, 5643, 5694, 5742, 5784, 5827, 5881, 5941, 5997, 6049, 6104, 6139, 6166, 6193]

axis=plt.gca()
starttime=datetime.datetime(2020,3,1)
endtime=datetime.datetime(2020,4,7)
jianju=datetime.timedelta(days=1)
daes=mat.dates.drange(starttime,endtime,jianju)
axis=plt.gca()
dateFormat=mat.dates.DateFormatter('%Y-%m-%d')
axis.xaxis.set_major_formatter(dateFormat)
axis.plot(daes,x1)
axis.plot(daes,x2)
plt.xlabel('Date')
plt.ylabel('Confirmed_number')
legend=plt.legend(["male","female"])

Visualize province and 'confirmed' in TimeProvince.csv

In [49]:
import csv
cout1=[]
cout2=[]
cout3=[]
cout4=[]
cout5=[]
cout6=[]
cout7=[]
cout8=[]
cout9=[]
cout10=[]
cout11=[]
cout12=[]
cout13=[]
cout14=[]
cout15=[]
cout16=[]
cout17=[]
with open(r"C:\Users\hp\Desktop\TimeProvince.csv",'r') as csvfile:
    reader = csv.DictReader(csvfile)
    for row in reader:
        if(row["province"]=='Seoul'):
            cout1.append(int(row["confirmed"]))
        if(row["province"]=='Busan'):
            cout2.append(int(row["confirmed"]))
        if(row["province"]=='Daegu'):
            cout3.append(int(row["confirmed"]))
        if(row["province"]=='Incheon'):
            cout4.append(int(row["confirmed"]))
        if(row["province"]=='Gwangju'):
            cout5.append(int(row["confirmed"]))
        if(row["province"]=='Daejeon'):
            cout6.append(int(row["confirmed"]))
        if(row["province"]=='Ulsan'):
            cout7.append(int(row["confirmed"]))
        if(row["province"]=='Sejong'):
            cout8.append(int(row["confirmed"]))
        if(row["province"]=='Gyeonggi-do'):
            cout9.append(int(row["confirmed"]))
        if(row["province"]=='Gangwon-do'):
            cout10.append(int(row["confirmed"]))
        if(row["province"]=='Chungcheongbuk-do'):
            cout11.append(int(row["confirmed"]))
        if(row["province"]=='Chungcheongnam-do'):
            cout12.append(int(row["confirmed"]))
        if(row["province"]=='Jeollabuk-do'):
            cout13.append(int(row["confirmed"]))
        if(row["province"]=='Jeollanam-do'):
            cout14.append(int(row["confirmed"]))
        if(row["province"]=='Gyeongsangbuk-do'):
            cout15.append(int(row["confirmed"]))
        if(row["province"]=='Gyeongsangnam-do'):
            cout16.append(int(row["confirmed"]))
        if(row["province"]=='Jeju-do'):
            cout17.append(int(row["confirmed"]))


print(cout1)
print(len(cout1))
print(cout2)
print(len(cout2))
print(cout3)
print(len(cout3))
print(cout4)
print(len(cout4))
print(cout5)
print(len(cout5))
print(cout6)
print(len(cout6))
print(cout7)
print(len(cout7))
print(cout8)
print(len(cout8))
print(cout9)
print(len(cout9))
print(cout10)
print(len(cout10))
print(cout11)
print(len(cout11))
print(cout12)
print(len(cout12))
print(cout13)
print(len(cout13))
print(cout14)
print(len(cout14))
print(cout15)
print(len(cout15))
print(cout16)
print(len(cout16))
print(cout17)
print(len(cout17))

[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 3, 4, 4, 5, 5, 5, 5, 10, 11, 11, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 20, 22, 27, 30, 31, 35, 41, 49, 56, 62, 77, 87, 91, 98, 99, 103, 105, 108, 120, 130, 141, 193, 212, 225, 238, 247, 253, 265, 270, 282, 299, 314, 324, 330, 334, 347, 360, 372, 390, 410, 426, 450, 474, 488, 506, 528, 552, 563, 567]
79
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 11, 19, 34, 42, 58, 61, 65, 80, 83, 88, 90, 92, 92, 95, 96, 96, 96, 96, 98, 99, 100, 103, 106, 107, 107, 107, 107, 108, 108, 108, 109, 111, 112, 112, 113, 114, 117, 118, 119, 122, 122, 122, 122, 122, 122, 123]
79
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 11, 30, 110, 193, 310, 483, 543, 710, 1132, 1580, 2236, 2704, 3081, 3601, 4007, 4328, 4694, 5084, 5381, 5571, 5663, 5794, 5867, 5928, 5990, 6031, 6066, 6098, 6144, 6241, 6275, 6344, 6387, 6411, 6442, 6456, 6482, 6516, 6587, 6610, 6624, 6684, 6704, 6725, 6734, 6761, 6768, 6781, 6794]
79
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 3, 3, 4, 6, 6, 7, 7, 9, 9, 9, 9, 9, 9, 13, 25, 25, 27, 28, 30, 30, 31, 32, 32, 36, 40, 40, 40, 41, 42, 43, 46, 51, 58, 58, 64, 69, 73, 74, 77, 79, 80, 80]
79
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 6, 8, 9, 9, 9, 9, 9, 9, 9, 9, 11, 13, 13, 13, 13, 13, 15, 15, 15, 15, 15, 15, 16, 16, 16, 17, 17, 18, 18, 19, 19, 19, 19, 19, 20, 20, 20, 20, 20, 24, 25, 26, 26, 27, 27, 27]
79
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 3, 3, 3, 5, 9, 13, 13, 13, 14, 14, 15, 16, 18, 18, 18, 18, 18, 18, 20, 22, 22, 22, 22, 22, 22, 22, 22, 22, 24, 24, 24, 24, 30, 31, 31, 34, 34, 36, 36, 36, 36, 36, 37, 39, 39]
79
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 2, 3, 4, 11, 14, 17, 20, 20, 20, 23, 23, 23, 23, 24, 24, 24, 25, 25, 27, 27, 28, 28, 28, 30, 36, 36, 36, 36, 36, 36, 37, 37, 39, 39, 39, 39, 39, 39, 40, 40, 40, 40, 40, 40]
79
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 6, 8, 10, 15, 32, 38, 39, 40, 40, 41, 41, 41, 41, 41, 42, 42, 44, 44, 44, 44, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46]
79
[0, 0, 0, 0, 1, 1, 2, 2, 2, 2, 3, 5, 6, 8, 8, 8, 10, 10, 10, 10, 10, 10, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 14, 20, 24, 35, 42, 51, 62, 72, 82, 89, 92, 94, 101, 110, 120, 130, 141, 153, 163, 175, 178, 185, 200, 211, 231, 262, 277, 295, 309, 321, 337, 351, 366, 387, 401, 412, 433, 448, 463, 476, 499, 516, 539, 562, 572, 580, 590]
79
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 6, 6, 6, 6, 7, 7, 15, 19, 20, 21, 23, 25, 26, 27, 28, 28, 29, 29, 29, 29, 29, 29, 29, 30, 30, 30, 30, 30, 30, 30, 31, 31, 31, 32, 34, 36, 36, 38, 39, 41, 42, 45, 45, 47]
79
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 3, 3, 3, 5, 5, 8, 9, 10, 11, 11, 11, 11, 12, 15, 20, 24, 25, 25, 27, 27, 27, 28, 31, 31, 31, 32, 33, 33, 34, 35, 37, 38, 38, 39, 41, 41, 41, 44, 44, 44, 44, 45, 45, 45, 45, 45]
79
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 2, 3, 12, 35, 55, 68, 78, 81, 82, 86, 90, 92, 98, 102, 104, 112, 114, 115, 115, 115, 115, 115, 118, 118, 119, 119, 120, 120, 120, 123, 124, 124, 126, 127, 127, 128, 131, 133, 134, 135, 135, 136, 137]
79
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 3, 3, 3, 3, 5, 5, 5, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 12, 13, 13, 14, 14, 15, 15, 16, 16, 16]
79
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 3, 3, 5, 5, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 6, 6, 6, 8, 8, 8, 8, 9, 9, 9, 12, 14, 15, 15, 15, 15, 15]
79
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 25, 28, 145, 170, 193, 249, 317, 345, 409, 488, 556, 624, 685, 774, 861, 984, 1049, 1082, 1107, 1117, 1135, 1143, 1147, 1153, 1157, 1164, 1169, 1178, 1190, 1203, 1243, 1254, 1256, 1257, 1262, 1274, 1283, 1285, 1287, 1298, 1300, 1302, 1304, 1309, 1310, 1314, 1316, 1317]
79
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 7, 7, 17, 20, 23, 34, 43, 49, 59, 63, 64, 64, 65, 74, 77, 82, 83, 83, 83, 84, 85, 85, 85, 85, 85, 86, 86, 86, 87, 87, 88, 89, 90, 90, 90, 91, 91, 94, 95, 96, 100, 106, 107, 108, 109, 111, 112]
79
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 6, 6, 7, 8, 8, 9, 9, 9, 9, 9, 9, 12, 12, 12]
79
In [50]:
import matplotlib as mat
import datetime
f=plt.figure(figsize=(15,8))
p1=[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 3, 4, 4, 5, 5, 5, 5, 10, 11, 11, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 20, 22, 27, 30, 31, 35, 41, 49, 56, 62, 77, 87, 91, 98, 99, 103, 105, 108, 120, 130, 141, 193, 212, 225, 238, 247, 253, 265, 270, 282, 299, 314, 324, 330, 334, 347, 360, 372, 390, 410, 426, 450, 474, 488, 506, 528, 552, 563, 567]
p2=[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 11, 19, 34, 42, 58, 61, 65, 80, 83, 88, 90, 92, 92, 95, 96, 96, 96, 96, 98, 99, 100, 103, 106, 107, 107, 107, 107, 108, 108, 108, 109, 111, 112, 112, 113, 114, 117, 118, 119, 122, 122, 122, 122, 122, 122, 123]
p3=[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 11, 30, 110, 193, 310, 483, 543, 710, 1132, 1580, 2236, 2704, 3081, 3601, 4007, 4328, 4694, 5084, 5381, 5571, 5663, 5794, 5867, 5928, 5990, 6031, 6066, 6098, 6144, 6241, 6275, 6344, 6387, 6411, 6442, 6456, 6482, 6516, 6587, 6610, 6624, 6684, 6704, 6725, 6734, 6761, 6768, 6781, 6794]
p4=[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 3, 3, 4, 6, 6, 7, 7, 9, 9, 9, 9, 9, 9, 13, 25, 25, 27, 28, 30, 30, 31, 32, 32, 36, 40, 40, 40, 41, 42, 43, 46, 51, 58, 58, 64, 69, 73, 74, 77, 79, 80, 80]
p5=[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 6, 8, 9, 9, 9, 9, 9, 9, 9, 9, 11, 13, 13, 13, 13, 13, 15, 15, 15, 15, 15, 15, 16, 16, 16, 17, 17, 18, 18, 19, 19, 19, 19, 19, 20, 20, 20, 20, 20, 24, 25, 26, 26, 27, 27, 27]
p6=[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 3, 3, 3, 5, 9, 13, 13, 13, 14, 14, 15, 16, 18, 18, 18, 18, 18, 18, 20, 22, 22, 22, 22, 22, 22, 22, 22, 22, 24, 24, 24, 24, 30, 31, 31, 34, 34, 36, 36, 36, 36, 36, 37, 39, 39]
p7=[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 2, 3, 4, 11, 14, 17, 20, 20, 20, 23, 23, 23, 23, 24, 24, 24, 25, 25, 27, 27, 28, 28, 28, 30, 36, 36, 36, 36, 36, 36, 37, 37, 39, 39, 39, 39, 39, 39, 40, 40, 40, 40, 40, 40]
p8=[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 6, 8, 10, 15, 32, 38, 39, 40, 40, 41, 41, 41, 41, 41, 42, 42, 44, 44, 44, 44, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46]
p9=[0, 0, 0, 0, 1, 1, 2, 2, 2, 2, 3, 5, 6, 8, 8, 8, 10, 10, 10, 10, 10, 10, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 14, 20, 24, 35, 42, 51, 62, 72, 82, 89, 92, 94, 101, 110, 120, 130, 141, 153, 163, 175, 178, 185, 200, 211, 231, 262, 277, 295, 309, 321, 337, 351, 366, 387, 401, 412, 433, 448, 463, 476, 499, 516, 539, 562, 572, 580, 590]
p10=[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 6, 6, 6, 6, 7, 7, 15, 19, 20, 21, 23, 25, 26, 27, 28, 28, 29, 29, 29, 29, 29, 29, 29, 30, 30, 30, 30, 30, 30, 30, 31, 31, 31, 32, 34, 36, 36, 38, 39, 41, 42, 45, 45, 47]
p11=[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 3, 3, 3, 5, 5, 8, 9, 10, 11, 11, 11, 11, 12, 15, 20, 24, 25, 25, 27, 27, 27, 28, 31, 31, 31, 32, 33, 33, 34, 35, 37, 38, 38, 39, 41, 41, 41, 44, 44, 44, 44, 45, 45, 45, 45, 45]
p12=[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 2, 3, 12, 35, 55, 68, 78, 81, 82, 86, 90, 92, 98, 102, 104, 112, 114, 115, 115, 115, 115, 115, 118, 118, 119, 119, 120, 120, 120, 123, 124, 124, 126, 127, 127, 128, 131, 133, 134, 135, 135, 136, 137]
p13=[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 3, 3, 3, 3, 5, 5, 5, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 12, 13, 13, 14, 14, 15, 15, 16, 16, 16]
p14=[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 3, 3, 5, 5, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 6, 6, 6, 8, 8, 8, 8, 9, 9, 9, 12, 14, 15, 15, 15, 15, 15]
p15=[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 25, 28, 145, 170, 193, 249, 317, 345, 409, 488, 556, 624, 685, 774, 861, 984, 1049, 1082, 1107, 1117, 1135, 1143, 1147, 1153, 1157, 1164, 1169, 1178, 1190, 1203, 1243, 1254, 1256, 1257, 1262, 1274, 1283, 1285, 1287, 1298, 1300, 1302, 1304, 1309, 1310, 1314, 1316, 1317]
p16=[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 7, 7, 17, 20, 23, 34, 43, 49, 59, 63, 64, 64, 65, 74, 77, 82, 83, 83, 83, 84, 85, 85, 85, 85, 85, 86, 86, 86, 87, 87, 88, 89, 90, 90, 90, 91, 91, 94, 95, 96, 100, 106, 107, 108, 109, 111, 112]
p17=[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 6, 6, 7, 8, 8, 9, 9, 9, 9, 9, 9, 12, 12, 12]

axis=plt.gca()
starttime=datetime.datetime(2020,1,19)
endtime=datetime.datetime(2020,4,7)
jianju=datetime.timedelta(days=1)
daes=mat.dates.drange(starttime,endtime,jianju)
axis=plt.gca()
dateFormat=mat.dates.DateFormatter('%Y-%m-%d')
axis.xaxis.set_major_formatter(dateFormat)
axis.plot(daes,p1)
axis.plot(daes,p2)
axis.plot(daes,p3)
axis.plot(daes,p4)
axis.plot(daes,p5)
axis.plot(daes,p6)
axis.plot(daes,p7)
axis.plot(daes,p8)
axis.plot(daes,p9)
axis.plot(daes,p10)
axis.plot(daes,p11)
axis.plot(daes,p12)
axis.plot(daes,p13)
axis.plot(daes,p14)
axis.plot(daes,p15)
axis.plot(daes,p16)
axis.plot(daes,p17)
legend=plt.legend(["Seoul","Busan","Daegu","Incheon","Gwangju","Daejeon","Ulsan","Sejong","Gyeonggi-do","Gangwon-do","Chungcheongbuk-do","Chungcheongnam-do","Jeollabuk-do","Jeollanam-do","Gyeongsangbuk-do","Gyeongsangnam-do","Jeju-do"])
plt.xlabel('Date')
plt.ylabel('Different_province_confirmed')
Out[50]:
Text(0, 0.5, 'Different_province_confirmed')
Document_TASK2

Mandatory Task2

Prediction of the recovery state of the patients

In this part,I mainly did following works:

  • Accessment of recovery sate
    • Classification: Released or Dicreased
    • Regression: Recovery time
  • Exploring important features

Read file

Check the situation,and extract columns that may needed.

In [41]:
#Read file and check the situation
import pandas as pd
patientInfo = pd.read_csv('PatientInfo.csv')
print(patientInfo.shape)
#patientInfo.head(5)
print(patientInfo.info())
print(patientInfo.isnull().any(axis=0))
#extrect the columns needed
sub_set = patientInfo[['sex','age','province','city','disease','infection_order','confirmed_date','released_date','deceased_date','state']]

(3128, 18)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3128 entries, 0 to 3127
Data columns (total 18 columns):
patient_id            3128 non-null int64
global_num            1968 non-null float64
sex                   3034 non-null object
birth_year            2664 non-null float64
age                   3023 non-null object
country               3038 non-null object
province              3128 non-null object
city                  3052 non-null object
disease               18 non-null object
infection_case        2309 non-null object
infection_order       31 non-null float64
infected_by           735 non-null float64
contact_number        589 non-null float64
symptom_onset_date    446 non-null object
confirmed_date        3128 non-null object
released_date         981 non-null object
deceased_date         56 non-null object
state                 3128 non-null object
dtypes: float64(5), int64(1), object(12)
memory usage: 440.0+ KB
None
patient_id            False
global_num             True
sex                    True
birth_year             True
age                    True
country                True
province              False
city                   True
disease                True
infection_case         True
infection_order        True
infected_by            True
contact_number         True
symptom_onset_date     True
confirmed_date        False
released_date          True
deceased_date          True
state                 False
dtype: bool

Classification

  1. Data processing
    • Deal with null value: Since the missing values in age and gender are rare, I directly delete those rows. Other missing values are filled by different means.
    • Extract the columns needed again, mapping objects to float type or int64 type.
In [42]:
#missing values
#method1
for i in range(len(sub_set)):
  if pd.isna(sub_set['age'][i]):
    sub_set = sub_set.drop(i)

#method2
sub_set['sex'] = sub_set['sex'].fillna('233')
na_loc = sub_set[(sub_set.sex == '233')].index.tolist()
sub_set = sub_set.drop(na_loc)
# fill NA 
sub_set['disease'] = sub_set['disease'].fillna('False')
print(sub_set['infection_order'].value_counts())# Check its mode
sub_set['infection_order'] = sub_set['infection_order'].fillna(1.0) #fill na with mode

1.0    11
2.0     8
3.0     6
5.0     3
4.0     2
6.0     1
Name: infection_order, dtype: int64
In [43]:
#Extract the columns needed again, mapping objects to float type or int64 type.
sub_set1 = sub_set[['sex','age','disease','infection_order','province','state']]
sub_set1.head(10)
#print(sub_set1['state'].value_counts())

province = sub_set1.loc[:,'province'].drop_duplicates().values.tolist()
pro_dict = dict(zip(province,range(0,len(province))))
#pro_dict

TF_dict = {True:1,'False':0}
sex_dict = {'male':0,'female':1}
age_dict = {'0s':0,'10s':1,'20s':2,'30s':3,'40s':4,'50s':5,'60s':6,'70s':7,'80s':8,'90s':9,'100s':10,'66s':6}
state_dict = {'isolated':1,'released':1,'deceased':0}
sub_set1['sex'] = sub_set1['sex'].map(sex_dict)
sub_set1['age'] = sub_set1['age'].map(age_dict)
sub_set1['disease'] = sub_set1['disease'].map(TF_dict)
sub_set1['province'] = sub_set1['province'].map(pro_dict)
sub_set1['state'] = sub_set1['state'].map(state_dict)

D:\Anaconda\lib\site-packages\ipykernel_launcher.py:14: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

D:\Anaconda\lib\site-packages\ipykernel_launcher.py:15: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  from ipykernel import kernelapp as app
D:\Anaconda\lib\site-packages\ipykernel_launcher.py:16: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  app.launch_new_instance()
D:\Anaconda\lib\site-packages\ipykernel_launcher.py:17: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
D:\Anaconda\lib\site-packages\ipykernel_launcher.py:18: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

Show the data processing result: 5 features with 1 label

In [44]:
sub_set1.head(5)
Out[44]:
sex age disease infection_order province state
0 0 5 0 1.0 0 1
1 0 3 0 1.0 0 1
2 0 5 0 2.0 0 1
3 0 2 0 1.0 0 1
4 1 2 0 2.0 0 1
  1. Dataset splitting and model fitting
    • Split data: train:test = 7:3
    • Fit model: DecisionTreeClassifier
    • Assessment and visualization
In [45]:
from sklearn.model_selection import train_test_split
y = sub_set1['state'] #traning result 
del sub_set1['state']
X  = sub_set1 #transing 
df_trainx,df_testx, df_trainy, df_testy = train_test_split(X, y, test_size=0.3, random_state=0)

print(df_trainy.value_counts()/len(df_trainy))
print(df_testy.value_counts()/len(df_testy))

1    0.982033
0    0.017967
Name: state, dtype: float64
1    0.974642
0    0.025358
Name: state, dtype: float64
In [46]:
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
model = DecisionTreeClassifier(criterion='entropy',splitter = 'best',max_depth=5)
#model = KNeighborsClassifier(n_neighbors = 1)
model.fit(df_trainx, df_trainy)
quility_test_pred = model.predict(df_testx)

print("train score:", model.score(df_trainx, df_trainy))
print("test score:", model.score(df_testx, df_testy))

train score: 0.9895981087470449
test score: 0.9834619625137817
In [47]:
from sklearn import metrics
print(metrics.classification_report(df_testy, quility_test_pred))
print(metrics.confusion_matrix(df_testy, quility_test_pred))
print(metrics.accuracy_score(df_testy, quility_test_pred))

              precision    recall  f1-score   support

           0       0.90      0.39      0.55        23
           1       0.98      1.00      0.99       884

    accuracy                           0.98       907
   macro avg       0.94      0.70      0.77       907
weighted avg       0.98      0.98      0.98       907

[[  9  14]
 [  1 883]]
0.9834619625137817
In [48]:
from sklearn.externals.six import StringIO
from IPython.display import Image
from sklearn import tree
import pydot
#dot_data = StringIO()
#tree.export_graphviz(model, out_file = dot_data, feature_names = df_trainx.columns, 
 #                    class_names = ['no_default', 'default'], filled = True, rounded = True, special_characters = True)
#graph = pydot.graph_from_dot_data(dot_data.getvalue())
#graph[0].write_png("credit.png")
#Image(graph[0].create_png())
from IPython.display import Image
from sklearn import tree
import pydotplus

#regr_1 和regr_2
dot_data = tree.export_graphviz(model, out_file=None,  #regr_1 是对应分类器
                         feature_names=df_trainx.columns,   #对应特征的名字
                         class_names= ['sorry', 'alive'],    #对应类别的名字
                         filled=True, rounded=True,
                         special_characters=True)
graph = pydotplus.graph_from_dot_data(dot_data)
graph.write_png('example.png')    #保存图像
Image(graph.create_png())
Out[48]:

Regression

  1. Data processing
    • Extract the columns needed again,deal with missing value.
    • Get recovery time.
    • mapping objects to float type or int64 type. （similar as steps above）
In [49]:
# Extract the columns needed again,deal with missing value.
sub_set2 = sub_set[['sex','age','disease','infection_order','province','confirmed_date','released_date']]
sub_set2['released_date'] = sub_set2['released_date'].fillna('466')
N_released = sub_set2[(sub_set2.released_date =='466')].index.tolist()
sub_set2 = sub_set2.drop(N_released)
print(sub_set2.info())

<class 'pandas.core.frame.DataFrame'>
Int64Index: 979 entries, 0 to 3122
Data columns (total 7 columns):
sex                979 non-null object
age                979 non-null object
disease            979 non-null object
infection_order    979 non-null float64
province           979 non-null object
confirmed_date     979 non-null object
released_date      979 non-null object
dtypes: float64(1), object(6)
memory usage: 61.2+ KB
None

D:\Anaconda\lib\site-packages\ipykernel_launcher.py:3: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  This is separate from the ipykernel package so we can avoid doing imports until
In [50]:
#Get recovery time
import numpy as np
from pandas import to_datetime
from datetime import datetime
sub_set2.head(20)
start_date = to_datetime(sub_set2.confirmed_date,format="%Y-%m-%d")
end_date = to_datetime(sub_set2.released_date,format="%Y-%m-%d")
#print(start_date)
#print(end_date)
recover_time = end_date - start_date
recover_time = recover_time.map(lambda x: x/np.timedelta64(1,'D'))
sub_set2['recover_time'] = recover_time
sub_set2 = sub_set2.drop(columns=['confirmed_date','released_date'])
sub_set2.head(5)
Out[50]:
sex age disease infection_order province recover_time
0 male 50s False 1.0 Seoul 13.0
1 male 30s False 1.0 Seoul 32.0
2 male 50s False 2.0 Seoul 20.0
3 male 20s False 1.0 Seoul 16.0
4 female 20s False 2.0 Seoul 24.0
In [51]:
#mapping objects to float type or int64 type.
province = sub_set2.loc[:,'province'].drop_duplicates().values.tolist()
pro_dict = dict(zip(province,range(0,len(province))))
pro_dict
TF_dict = {True:1,'False':0}
sex_dict = {'male':0,'female':1}
age_dict = {'0s':0,'10s':1,'20s':2,'30s':3,'40s':4,'50s':5,'60s':6,'70s':7,'80s':8,'90s':9,'100s':10,'66s':6}
state_dict = {'isolated':1,'released':1,'deceased':0}
sub_set2['sex'] = sub_set2['sex'].map(sex_dict)
sub_set2['age'] = sub_set2['age'].map(age_dict)
sub_set2['disease'] = sub_set2['disease'].map(TF_dict)
sub_set2['province'] = sub_set2['province'].map(pro_dict)

Show the data processing result: 5 features with 1 label

In [52]:
sub_set2.head(5)
Out[52]:
sex age disease infection_order province recover_time
0 0 5 0 1.0 0 13.0
1 0 3 0 1.0 0 32.0
2 0 5 0 2.0 0 20.0
3 0 2 0 1.0 0 16.0
4 1 2 0 2.0 0 24.0
  1. Dataset splitting and model fitting
    • Split data: train:test = 7:3
    • Fit model: Lasso regression
    • Assessment
In [53]:
from sklearn.model_selection import train_test_split
y = sub_set2['recover_time'] #训练结果
del sub_set2['recover_time']
X  = sub_set2 #训练数据
df_trainx,df_testx, df_trainy, df_testy = train_test_split(X, y, test_size=0.3, random_state=0)

print(df_trainy)
print(df_testy)

2798    35.0
1844    34.0
1919    19.0
1695    35.0
770     11.0
        ...
2539    15.0
1045    27.0
2139    15.0
2063    16.0
2209    23.0
Name: recover_time, Length: 685, dtype: float64
1688    20.0
1942    28.0
2357    21.0
1963    24.0
2808    26.0
        ...
2372    15.0
2925    23.0
2910    25.0
3052    18.0
27      17.0
Name: recover_time, Length: 294, dtype: float64
In [54]:
from sklearn.linear_model import Lasso,LassoCV,LassoLarsCV
#from sklearn.neighbors import KNeighborsRegressor
#from sklearn.linear_model import LinearRegression 
model = LassoCV()
#model = LinearRegression()
model.fit(X, y)   # linear regression model 
print('matrix:\n',model.coef_)
print('model:\n',model)
predicted = model.predict(df_testx)

from sklearn.metrics import r2_score
r2_score(df_testy, predicted)

D:\Anaconda\lib\site-packages\sklearn\model_selection\_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.
  warnings.warn(CV_WARNING, FutureWarning)

matrix:
 [-0.          0.24035699  0.          0.          0.40955219]
model:
 LassoCV(alphas=None, copy_X=True, cv='warn', eps=0.001, fit_intercept=True,
        max_iter=1000, n_alphas=100, n_jobs=None, normalize=False,
        positive=False, precompute='auto', random_state=None,
        selection='cyclic', tol=0.0001, verbose=False)
Out[54]:
0.043056521202346154

Assessment and

  • Interpretation for classification part

    • I also tried Naive Bayes and KNN classifier,found that decision tree performs best.

      In these 3 classifiers, the decreased situation are all undervalued, at first I assumed that it's because I counted those isolated into alive case,however part of them may decrease soon. then I tried to remove those isolation cases,the model only performed worse.

      Another origin of misclassification may come from the encode method of feature-province ,I just encoded it with the present order, I hope to optimize this part after gaining the the point for each cities.

    • Accroding to precision and recall,the decreased probability is undervalued, but still the result still has value since it has a good overall score.

  • Interpretation for regression part

    • I also tried different means of regression(KNN, linear regression), Lasso do the best,How ever the result is still meaningless. The r2 score is 0.04, far away from 1.
    • Still I assume that improve the encode method of province may positively effect on the result.

Exploring important features

  • Using PCA to do the dimension reduction:
    • Take region difference into account, merge region information to patientInfo.
    • Standardization of the data.
    • Applying the packaged PCA processes
  • Result
    • Those datas are reduced to 3 dimensional, containing about 82% of origin information.
In [55]:
Region = pd.read_csv('Region.csv')
del Region['code']
del Region['latitude']
#sub_set
merge_result = pd.merge(sub_set, Region, on='city')
#merge_result
# found contradiction in 2 colums of provience
sub_set3 =  merge_result.copy(deep=True)
dllist = ['province_y','deceased_date','released_date','confirmed_date','city','state','province_x','longitude']
for item in dllist:
    del sub_set3[item]
sub_set3
Out[55]:
sex age disease infection_order elementary_school_count kindergarten_count university_count academy_ratio elderly_population_ratio elderly_alone_ratio nursing_home_count
0 male 50s False 1.0 36 56 1 1.17 14.39 5.7 1080
1 male 50s False 1.0 17 21 0 1.43 11.84 5.0 147
2 male 50s False 1.0 36 56 1 1.17 14.39 5.7 1080
3 male 50s False 1.0 17 21 0 1.43 11.84 5.0 147
4 male 30s False 1.0 36 56 1 1.17 14.39 5.7 1080
... ... ... ... ... ... ... ... ... ... ... ...
3339 female 20s False 1.0 113 123 4 1.53 15.10 6.4 1245
3340 male 30s False 1.0 113 123 4 1.53 15.10 6.4 1245
3341 female 20s False 1.0 113 123 4 1.53 15.10 6.4 1245
3342 female 10s False 1.0 113 123 4 1.53 15.10 6.4 1245
3343 female 20s False 1.0 113 123 4 1.53 15.10 6.4 1245

3344 rows × 11 columns

In [56]:
#mapping using same procedure

# TF_dict = {True:1,'False':0}
# sex_dict = {'male':0,'female':1}
# age_dict = {'0s':0,'10s':1,'20s':2,'30s':3,'40s':4,'50s':5,'60s':6,'70s':7,'80s':8,'90s':9,'100s':10,'66s':6}
# state_dict = {'isolated':1,'released':1,'deceased':0}
sub_set3['sex'] = sub_set3['sex'].map(sex_dict)
sub_set3['age'] = sub_set3['age'].map(age_dict)
sub_set3['disease'] = sub_set3['disease'].map(TF_dict)
#sub_set3['state'] = sub_set3['state'].map(state_dict)
# stdlist = ['elementary_school_count','kindergarten_count','university_count',
#            'academy_ratio','elderly_population_ratio','elderly_alone_ratio','nursing_home_count']
# for item in stdlist:
# sub_set3 = (sub_set3-sub_set3.mean())/(sub_set3.std())  
sub_set3 = (sub_set3-sub_set3.min())/(sub_set3.max()-sub_set3.min())
sub_set3.head(5)
#finish
Out[56]:
sex age disease infection_order elementary_school_count kindergarten_count university_count academy_ratio elderly_population_ratio elderly_alone_ratio nursing_home_count
0 0.0 0.5 0.0 0.0 0.293578 0.272251 0.1 0.220207 0.205711 0.112150 0.344648
1 0.0 0.5 0.0 0.0 0.119266 0.089005 0.0 0.287565 0.127418 0.079439 0.040144
2 0.0 0.5 0.0 0.0 0.293578 0.272251 0.1 0.220207 0.205711 0.112150 0.344648
3 0.0 0.5 0.0 0.0 0.119266 0.089005 0.0 0.287565 0.127418 0.079439 0.040144
4 0.0 0.3 0.0 0.0 0.293578 0.272251 0.1 0.220207 0.205711 0.112150 0.344648
In [63]:
from sklearn.decomposition import PCA

pca = PCA(n_components=3)
newsub_set3 = pca.fit_transform(sub_set3)
print(pca.explained_variance_ratio_)
newsub_set3

[0.38179656 0.2822589  0.15891612]
Out[63]:
array([[ 0.58768047,  0.0829891 ,  0.19583255],
       [ 0.5890437 ,  0.31137345,  0.13733238],
       [ 0.58768047,  0.0829891 ,  0.19583255],
       ...,
       [-0.40019993, -0.53567967,  0.35243724],
       [-0.39547897, -0.54561411,  0.36641845],
       [-0.40019993, -0.53567967,  0.35243724]])
Untitled12

Suggested Task3: Say something about the risk levels of the cities

In this task,I mainly analyze region.csv and take some characteristics to do the clustering process so that I can set different cities as different risk levels.

Deal with Region.csv

In [1]:
from sklearn.cluster import KMeans
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
loan_data = pd.read_csv(r"C:\Users\hp\Desktop\Region.csv")

Fistly,I count the number of Patients in every city from PatientInfo.csv and set a new column called 'counts'.Then I use 'counts' and 'nusing_home_count' to do K-means cluster to find something about risk level.

Then I need to find the most appropriate 'k' value by using Elbow Method

In [2]:
import matplotlib.pyplot as plt

#转换成numpy array
X = np.array(loan_data[['counts','nursing_home_count']])

distance = []
k = []
#簇的数量
for n_clusters in range(1,19):
    cls = KMeans(n_clusters).fit(X)

    #曼哈顿距离
    def manhattan_distance(x,y):
        return np.sum(abs(x-y))

    distance_sum = 0
    for i in range(n_clusters):
        group = cls.labels_ == i
        members = X[group,:]
        for v in members:
            distance_sum += manhattan_distance(np.array(v), cls.cluster_centers_[i])
    distance.append(distance_sum)
    k.append(n_clusters)
plt.scatter(k, distance)
plt.plot(k, distance)
plt.xlabel("k")
plt.ylabel("distance")
plt.show()

From the picture above,I choose 5 as 'k' value

In [3]:
loan = np.array(loan_data[['counts','nursing_home_count']])
clf=KMeans(n_clusters=5)
clf=clf.fit(loan)
In [4]:
#在原始数据表中增加聚类结果标签
loan_data['label']=clf.labels_
In [5]:
plt.figure(figsize=(10,8))
loan_data0=loan_data.loc[loan_data["label"] == 0]
loan_data1=loan_data.loc[loan_data["label"] == 1]
loan_data2=loan_data.loc[loan_data["label"] == 2]
loan_data3=loan_data.loc[loan_data["label"]==3]
loan_data4=loan_data.loc[loan_data["label"]==4]
#绘制聚类结果的散点图
plt.rc('font', family='STXihei', size=10)
plt.scatter(loan_data0['counts'],loan_data0['nursing_home_count'],50,color='#99CC01',marker='+',linewidth=2,alpha=0.8)
plt.scatter(loan_data1['counts'],loan_data1['nursing_home_count'],50,color='#FE0000',marker='+',linewidth=2,alpha=0.8)
plt.scatter(loan_data2['counts'],loan_data2['nursing_home_count'],50,color='#0000FE',marker='+',linewidth=2,alpha=0.8)
plt.scatter(loan_data3['counts'],loan_data3['nursing_home_count'],50,color='darkorange',marker='+',linewidth=2,alpha=0.8)
plt.scatter(loan_data4['counts'],loan_data4['nursing_home_count'],50,color='hotpink',marker='+',linewidth=2,alpha=0.8)
plt.xlabel('Patient_counts')
plt.ylabel('nursing_home_count')
plt.xlim(0,130)
plt.grid(color='#95a5a6',linestyle='--', linewidth=1,axis='both',alpha=0.4)
plt.show()

findfont: Font family ['STXihei'] not found. Falling back to DejaVu Sans.

From the clustering results above,I can set different clusters as different risk levels about patient_counts and nuring_hone_count.However,there are some cities with 0 patient_counts,so I consider them as lowest risk level temporarily.

In [6]:
from sklearn import metrics
print("Silhouette Coefficient：", metrics.silhouette_score(loan, clf.labels_, metric='euclidean'))

Silhouette Coefficient： 0.6556911851769226

Model assement:The silhouette cofficient is suitable for this classification.

Next,since schools are the most vulnerable to clusters of infections,I plus elementary_school_count,kindergarten_count and university_count as a new column as 'all_school_counts' for each city.I consider all_school_counts and patient_counts.

In [7]:
import matplotlib.pyplot as plt

#转换成numpy array
X = np.array(loan_data[['all_school_counts','counts']])

distance = []
k = []
#簇的数量
for n_clusters in range(1,19):
    cls = KMeans(n_clusters).fit(X)

    #曼哈顿距离
    def manhattan_distance(x,y):
        return np.sum(abs(x-y))

    distance_sum = 0
    for i in range(n_clusters):
        group = cls.labels_ == i
        members = X[group,:]
        for v in members:
            distance_sum += manhattan_distance(np.array(v), cls.cluster_centers_[i])
    distance.append(distance_sum)
    k.append(n_clusters)
plt.scatter(k, distance)
plt.plot(k, distance)
plt.xlabel("k")
plt.ylabel("distance")
plt.show()

I choose 5 as 'k' value

In [8]:
loan = np.array(loan_data[['all_school_counts','counts']])
#设置类别为5
clf=KMeans(n_clusters=5)
#将数据代入到聚类模型中
clf=clf.fit(loan)
In [9]:
#在原始数据表中增加聚类结果标签
loan_data['label']=clf.labels_
In [10]:
plt.figure(figsize=(10,8))
loan_data0=loan_data.loc[loan_data["label"] == 0]
loan_data1=loan_data.loc[loan_data["label"] == 1]
loan_data2=loan_data.loc[loan_data["label"] == 2]
loan_data3=loan_data.loc[loan_data["label"]==3]
loan_data4=loan_data.loc[loan_data["label"]==4]
#绘制聚类结果的散点图
plt.rc('font', family='STXihei', size=10)
plt.scatter(loan_data0['all_school_counts'],loan_data0['counts'],50,color='#99CC01',marker='+',linewidth=2,alpha=0.8)
plt.scatter(loan_data1['all_school_counts'],loan_data1['counts'],50,color='#FE0000',marker='+',linewidth=2,alpha=0.8)
plt.scatter(loan_data2['all_school_counts'],loan_data2['counts'],50,color='#0000FE',marker='+',linewidth=2,alpha=0.8)
plt.scatter(loan_data3['all_school_counts'],loan_data3['counts'],50,color='darkorange',marker='+',linewidth=2,alpha=0.8)
plt.scatter(loan_data4['all_school_counts'],loan_data4['counts'],50,color='hotpink',marker='+',linewidth=2,alpha=0.8)
plt.xlabel('all_school_counts')
plt.ylabel('patient_counts')
plt.xlim(0,1500)
plt.grid(color='#95a5a6',linestyle='--', linewidth=1,axis='both',alpha=0.4)
plt.show()

According to the clustering results,I set different risk levels and combine the last clustering result doing comprehensive assessment to set cities as diiferent segregations.

In [11]:
from sklearn import metrics
print("Silhouette Coefficient：", metrics.silhouette_score(loan, clf.labels_, metric='euclidean'))

Silhouette Coefficient： 0.7035689321174315

Model assement:The silhouette coefficient is suitable.These data can be an important reference for government.

TTsk4_fb

TASK4 Prediction of Trend

This depends on the social network, the public consensus about the emergency events, and more importantly the self-protection awareness of the people, and the government policy, etc. Try to integrate some of these effects into your model, and make a prediction of the trend of the infection numbers. You can use any model you like. The information from the data files ‘Weather.csv’, ‘SearchTrend.csv’, etc.could be used. Perhaps network model and time series analysis can be good for this task. The dynamical model based on differential equations are also welcome.

Modeling infectious disease

Since Covid-19 cause typical infections I Applying SERI model which is a classic model for infectious disease to analyse those data, the interpretation is as below:

Labels explaination

S: Susceptible Population, here means those unaffected Korea people

E：Population that have been infected but not shown symptom, having the ability to infect others,may convert to patients or simply automatically recovered.

I：Infected population, is sured to quit infected state after several days, turning into R population.

R：Population that won't infect or be infected , here means those recovered and dead.

a： an E/I infects a people per day, shown as a = kS to introduce the effect of S population.

b： Percentage of E turning into I

Equation built

Dailyadd = a(I+E)

dS= -a(I+E)

dE = a(I+E)- Dailyadd(8 days ago*)

dI = b×Dailyadd(8 days ago) - b×Dailyadd(18 days ago*)

dR = (1-b)Dailyadd(8 days ago)+ b×Dailyadd(18 days ago)

* : According to the information on the internet,the Covid-19 have average 8 days of incubation. According to the given data, the patient takes 18 days averagely to recover(or die).

In [29]:
import pandas as pd
Case = pd.read_csv('SearchTrend.csv')
search = Case['coronavirus'].tolist()

totalsearch = [0]
for index in range(len(search)):
    totalsearch.append(totalsearch[index]+search[index])

searchpoint = totalsearch[-61:]
minpoint = searchpoint[0]
maxpoint = searchpoint[60]
coefficient = []
for index in range(61):
    coefficient.append(1-((searchpoint[index]-minpoint)/(maxpoint-minpoint)*0.99))
#Assume that if people are fully awared , 99% of the infections can be avoid.

print(searchpoint)
#the first patient is found on Feb 5th, so the data starts from Feb 5th

[976.6638, 1008.37784, 1048.84398, 1084.92089, 1137.03323, 1155.17241, 1171.0823799999998, 1186.1671, 1203.11513, 1213.4598099999998, 1223.25887, 1234.24408, 1249.22353, 1289.9833899999999, 1349.2022399999998, 1421.3588899999997, 1506.5979599999998, 1589.4980999999998, 1669.3928799999999, 1749.34729, 1820.60997, 1891.68201, 1957.8882, 2018.92873, 2075.13295, 2136.0014800000004, 2183.1751500000005, 2223.0156800000004, 2256.2578600000006, 2285.8743300000006, 2312.7792500000005, 2339.2694500000007, 2369.061730000001, 2397.556870000001, 2421.5803300000007, 2442.1454400000007, 2461.4439500000008, 2478.069720000001, 2492.991360000001, 2509.882760000001, 2524.5910400000007, 2538.4954400000006, 2557.0009700000005, 2570.5645600000007, 2581.8456700000006, 2592.988970000001, 2604.555530000001, 2615.429470000001, 2625.947420000001, 2636.328470000001, 2646.487160000001, 2656.738210000001, 2665.6028500000007, 2674.2939500000007, 2682.5947000000006, 2690.3039200000007, 2697.810140000001, 2705.257550000001, 2711.2992700000013, 2716.8522800000014, 2727.7611300000012]

The part above is added after the model has been built, which introduce peoples's awareness of the situation. (Shown as coefficient)

The awareness is graded by the sum-up search amount calculated from 'SearchTrend.csv'

Initialize

In [30]:
N = 52230000 #Overall Korea population
#initialize 
S = [N]
E = [1]
I = [1]
R = [0]
Dayadd = [0]
totalcon = [0]

Setting parameters

The parameters are repeatedly adjusted to fit the true situation.

I divided the infectious into 3 stage, according to the incubating time and recovery time:

  • First patient appears, Latent cases are not noticed by the society.
  • First Latent cases turned into patient,the same time the government tight the policy.
  • First recovery(or dead) case appears.
In [31]:
a = 0.7
k = a/N
b = 0.6
Tr = 1

#Stage 1 
for index in range(8):

    add = k*S[index]*(I[index]+E[index])
    S.append(S[index]-add)
    E.append(E[index]+add)
    I.append(I[index]-0) #没有意义 因为没有算康复人数
    R.append(0)
    Dayadd.append(add)
    totalcon.append(sum(Dayadd))
#Stage 2 
k_ad = k*0.5 #government policy but the infection rate by half
for index in range(8,18):
    #i/10
    add = coefficient[index]*k_ad*S[index]*(I[index]+E[index])
    S.append(S[index]-add)
    E.append(E[index]+add-Dayadd[index-8])
    I.append(I[index]+b*Dayadd[index-8]-0)
    R.append(R[index]+(1-b)*Dayadd[index-8])
    Dayadd.append(add)
    totalcon.append(sum(Dayadd))
print(len(I))

19

Before step into the 3 stage, check the first two stages:

In [32]:
import matplotlib.pyplot as plt
f=plt.figure(facecolor = 'white')
plt.plot(range(19),I)
plt.show()
In [33]:
#Stage 3
for index in range(18,60):
    add = coefficient[index]*k_ad*S[index]*(I[index]+E[index])
    S.append(S[index]-add)
    E.append(E[index]+add-Dayadd[index-8])
    I.append(I[index]+b*Dayadd[index-8]-b*Dayadd[index-18])
    R.append(R[index]+(1-b)*Dayadd[index-8]+b*Dayadd[index-18])
    Dayadd.append(add)
    totalcon.append(sum(Dayadd))
In [34]:
# here introduce the data in reality, a direct copy from my partners' work
confirmed=[1, 1, 1, 1, 2, 2, 3, 4, 4, 4, 6, 11, 12, 15, 15, 16, 18, 23, 24, 24, 27, 27, 28, 28, 28, 28, 28, 29, 30, 31, 51, 104, 204, 433, 602, 833, 977, 1261, 1766, 2337, 3150, 3736, 4212, 4812, 5328, 5766, 6284, 6767, 7134, 7382, 7513, 7755, 7869, 7979, 8086, 8126, 8236, 8320, 8413, 8565, 8652, 8799, 8897, 8961, 9037, 9137, 9241, 9332, 9478, 9583, 9661, 9786, 9887, 9976, 10062, 10156, 10237, 10284, 10331]

Plot the assumed situation and real situation.

In [39]:
confirmedFC = confirmed[-61:]
f=plt.figure(figsize=(15,8),facecolor = 'white')
plt.plot(range(61),I)
plt.plot(range(61),totalcon)
#plt.plot(range(61),R)
plt.plot(range(61),confirmedFC)
legend=plt.legend(["Patients",'assumed confirmed','confirmed'])
plt.show()
#index =26
#print(k*S[index]*(I[index]+E[index]))

3116.0908923306997

Interpretation

According to the picture, the situation under the idealized model is shown in yellow and blue,while the true situation is in green.

Assumption

The model beautifully fit the situation at the first 30 days, and it seem that the data reaches its peak faster in model than in reality. I suppose this part of difference is caused by suspected case( like the early phase in Wuhan), which is not yet been digested by the medical system.

Next step

Since the line is increased linearly from the 30th to 60th days, I assume the examing capacity of the medical system is a constant.

Directly use least square to find the slope (digesting rate).

In [40]:
apart = confirmedFC[30:60]
f=plt.figure(facecolor = 'white')
plt.plot(range(30),apart)
plt.show()
In [38]:
def liner_fitting(data_x,data_y):
      size = len(data_x);
      i=0
      sum_xy=0
      sum_y=0
      sum_x=0
      sum_sqare_x=0
      average_x=0;
      average_y=0;
      while i<size:
          sum_xy+=data_x[i]*data_y[i];
          sum_y+=data_y[i]
          sum_x+=data_x[i]
          sum_sqare_x+=data_x[i]*data_x[i]
          i+=1
      average_x=sum_x/size
      average_y=sum_y/size
      return_k=(size*sum_xy-sum_x*sum_y)/(size*sum_sqare_x-sum_x*sum_x)
      return_b=average_y-average_x*return_k
      return [return_k,return_b]

solution = liner_fitting(range(30),apart)
print(solution)
print((totalcon[60]-solution[1])/solution[0])#about 56 days from the 30th day
print(Dayadd[60])

[103.77085650723025, 7380.12258064516]
56.09157357609011
7.898586475212419

Final result and discussion

Result

Since the new case present after the 60th day is small, and continuedly decrease.I recard it as none changed.

From the calculation above, it takes 56 days for the green line(reality situation) to surpass the yellow line, That means after about 26 days from April 4th, the confirmed case in Korea may stop to increase if they consist with their epidemic prevention strategies.

Discussion

  • The advance:
    • The model fitted the true situation and give reference in different dimension(infectious rate, epidemic prevention strategies etc.)
    • Easy to adjust or add new parameters for fitting the true situation.
  • The short come:
    • Some features in this model are ignored like the infect ability differences betweeen incubated case and patient case.
    • And since there are no report shows that a person can be infected twice or still have infect ability after recovered, the model do not distinguish recover and death.And the prediction in this realm is completely blank.
  • Something else
    • In the model, the parameters is under idealized condition, in which to fit the situation, the infectious of virus is too low compare with the reality. So I suppose that those who showed symptom is only a very small part of the infected case.
Untitled13

TA's Task4:

Analyze the location of the cases and patients, draw some conclusions Although different cities have different situations, citizens are not simply divided by the boundary of cities. According to the location info (latitude and longitude) of the files 'Case.csv' and 'PatientRoute.csv',can you find something hidden conclusions?For example, patients are grouped in some specific area,or someone may cause the infection of two groups of people,or cut the route of two cities that may ease the infection.

Our work mainly focus on the visualization of those data related to geographic position.

Firstly,Find several large clusters by latitude and longitude of PatientRoute.csv

In [1]:
from sklearn.cluster import KMeans
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
loan_data = pd.read_csv(r"C:\Users\hp\Desktop\PatientRoute.csv")
In [2]:
import numpy as np
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt

#转换成numpy array
X = np.array(loan_data[['latitude','longitude']])

distance = []
k = []
#簇的数量
for n_clusters in range(1,19):
    cls = KMeans(n_clusters).fit(X)

    #曼哈顿距离
    def manhattan_distance(x,y):
        return np.sum(abs(x-y))

    distance_sum = 0
    for i in range(n_clusters):
        group = cls.labels_ == i
        members = X[group,:]
        for v in members:
            distance_sum += manhattan_distance(np.array(v), cls.cluster_centers_[i])
    distance.append(distance_sum)
    k.append(n_clusters)
plt.scatter(k, distance)
plt.plot(k, distance)
plt.xlabel("k")
plt.ylabel("distance")
plt.show()

I choose 7 as 'k' value.

In [3]:
loan = np.array(loan_data[['latitude','longitude']])
clf=KMeans(n_clusters=7)
clf=clf.fit(loan)
In [4]:
#在原始数据表中增加聚类结果标签
loan_data['label']=clf.labels_
In [5]:
plt.figure(figsize=(10,8))
loan_data0=loan_data.loc[loan_data["label"]==0]
loan_data1=loan_data.loc[loan_data["label"]==1]
loan_data2=loan_data.loc[loan_data["label"]==2]
loan_data3=loan_data.loc[loan_data["label"]==3]
loan_data4=loan_data.loc[loan_data["label"]==4]
loan_data5=loan_data.loc[loan_data["label"]==5]
loan_data6=loan_data.loc[loan_data["label"]==6]

#绘制聚类结果的散点图
plt.rc('font', family='STXihei', size=10)
plt.scatter(loan_data0['latitude'],loan_data0['longitude'],50,color='#99CC01',marker='+',linewidth=2,alpha=0.8)
plt.scatter(loan_data1['latitude'],loan_data1['longitude'],50,color='#FE0000',marker='+',linewidth=2,alpha=0.8)
plt.scatter(loan_data2['latitude'],loan_data2['longitude'],50,color='#0000FE',marker='+',linewidth=2,alpha=0.8)
plt.scatter(loan_data3['latitude'],loan_data3['longitude'],50,color='darkorange',marker='+',linewidth=2,alpha=0.8)
plt.scatter(loan_data4['latitude'],loan_data4['longitude'],50,color='hotpink',marker='+',linewidth=2,alpha=0.8)
plt.scatter(loan_data5['latitude'],loan_data5['longitude'],50,color='slategray',marker='+',linewidth=2,alpha=0.8)
plt.scatter(loan_data6['latitude'],loan_data6['longitude'],50,color='yellow',marker='+',linewidth=2,alpha=0.8)
plt.xlabel('latitude')
plt.ylabel('longtitude')
plt.xlim(33,39)
plt.grid(color='#95a5a6',linestyle='--', linewidth=1,axis='both',alpha=0.4)
plt.show()

findfont: Font family ['STXihei'] not found. Falling back to DejaVu Sans.

The clustering results show different distribution of patient_route which can help government formulate guidelines for controlling the epidemic by these big clusters.

In [6]:
from sklearn import metrics
print("Silhouette Coefficient：", metrics.silhouette_score(loan, clf.labels_, metric='euclidean'))

Silhouette Coefficient： 0.7003794533443763

Model assement:The silhouette coefficient is good.So it can be an important factor to quarantine these areas so that avoiding case beginning to be worse.

Then we deal with case.csv and PatientRoute.csv at the same time.

TA_4(1)

Read file and Check the data

Also deleted those rows with empty value

In [1]:
import pandas as pd
Case = pd.read_csv('Case.csv')
print(Case.shape)
print(Case.info())
Case.head(5)
# delete NA
na_loc = Case[(Case.latitude == '-')].index.tolist()
Case = Case.drop(na_loc)
#print(Case['confirmed'].value_counts())

PatientRoute = pd.read_csv('PatientRoute.csv')
#print(PatientRoute.shape)
#print(PatientRoute.info())
PatientRoute.head(5)

(110, 8)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 110 entries, 0 to 109
Data columns (total 8 columns):
case_id           110 non-null int64
province          110 non-null object
city              110 non-null object
group             110 non-null bool
infection_case    110 non-null object
confirmed         110 non-null int64
latitude          110 non-null object
longitude         110 non-null object
dtypes: bool(1), int64(2), object(5)
memory usage: 6.2+ KB
None
Out[1]:
patient_id global_num date province city type latitude longitude
0 1000000001 2.0 2020-01-22 Gyeonggi-do Gimpo-si airport 37.615246 126.715632
1 1000000001 2.0 2020-01-24 Seoul Jung-gu hospital 37.567241 127.005659
2 1000000002 5.0 2020-01-25 Seoul Seongbuk-gu etc 37.592560 127.017048
3 1000000002 5.0 2020-01-26 Seoul Seongbuk-gu store 37.591810 127.016822
4 1000000002 5.0 2020-01-26 Seoul Seongdong-gu public_transportation 37.563992 127.029534

To get the coupled longitude and latitude data, and to count each couples' repeated time.

In [2]:
PatientRoute['Lo_La']=PatientRoute[['longitude','latitude']].apply(lambda x:"{:.4f},{:.4f}".format(*x),axis=1)
Lo_La_list = PatientRoute['Lo_La'].tolist()
Lo_La_set = set(Lo_La_list)
Lo_La_t = {}
for item in Lo_La_set:
    Lo_La_t[item] = 0
for item in Lo_La_list:
    Lo_La_t[item] +=1

case_lo = Case['longitude'].tolist()
case_la = Case['latitude'].tolist()
case_t = Case['confirmed'].tolist()
case_c = Case['infection_case'].tolist()
#Lo_La_t

LOLA = list(Lo_La_t)
time = list(Lo_La_t.values())
Lo_La = []
for item in LOLA:
    Lo_La.append(item.split(','))
#print(Lo_La)
#longitude and latitude are well placed here.

Show the data on the map:

In [24]:
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
#左下又上
plt.rcParams['figure.figsize'] = (22.0, 18.0)
map = Basemap(llcrnrlon =126, llcrnrlat = 34, urcrnrlon = 130, urcrnrlat=38.5)
map.readshapefile('./mapsituation/gis_osm_roads_free_1','state',color="grey") #this allow the road in Korea been painted .

map.drawcoastlines()
map.drawcountries()
for index in range(len(case_lo)):
    if case_t[index]>50:
        plt.scatter(float(case_lo[index]),float(case_la[index]),s = case_t[index]*3,color = 'm')
        plt.annotate(case_c[index],xy=(float(case_lo[index]),float(case_la[index])),fontsize= 20+case_t[index]/100)
for index in range(len(Lo_La)):
    if time[index]>9:
        plt.scatter(float(Lo_La[index][0]),float(Lo_La[index][1]),s = time[index]*3,color = 'blue')
# plt.show()
plt.show()

D:\Anaconda\lib\site-packages\ipykernel_launcher.py:5: MatplotlibDeprecationWarning:
The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.
  """
D:\Anaconda\lib\site-packages\ipykernel_launcher.py:6: MatplotlibDeprecationWarning:
The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.

D:\Anaconda\lib\site-packages\ipykernel_launcher.py:9: MatplotlibDeprecationWarning:
The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.
  if __name__ == '__main__':

Again without road condition

In [25]:
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
#
plt.rcParams['figure.figsize'] = (22.0, 18.0)
map = Basemap(llcrnrlon =126, llcrnrlat = 34, urcrnrlon = 130, urcrnrlat=38.5)
#map.readshapefile('./mapsituation/gis_osm_roads_free_1','state',color="grey") #this allow the road in Korea been painted .

map.drawcoastlines()
map.drawcountries()
for index in range(len(case_lo)):
    if case_t[index]>50:
        plt.scatter(float(case_lo[index]),float(case_la[index]),s = case_t[index]*3,color = 'm')
        plt.annotate(case_c[index],xy=(float(case_lo[index]),float(case_la[index])),fontsize= 20+case_t[index]/100)
for index in range(len(Lo_La)):
    if time[index]>9:
        plt.scatter(float(Lo_La[index][0]),float(Lo_La[index][1]),s = time[index]*3,color = 'blue')
# plt.show()
plt.show()

D:\Anaconda\lib\site-packages\ipykernel_launcher.py:5: MatplotlibDeprecationWarning:
The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.
  """
D:\Anaconda\lib\site-packages\ipykernel_launcher.py:9: MatplotlibDeprecationWarning:
The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.
  if __name__ == '__main__':